Qualification: 
Ph.D, M.Tech
v_sowmya@cb.amrita.edu

Dr. Sowmya V. currently serves as Assistant Professor at Amrita Center for Computational Engineering and Networking (CEN), Coimbatore Campus.

Professional Experience

  • Programmer Analyst Trainee at Cognizant Technology Solutions, Chennai (August 2010 -June 2011).
  • Assistant Professor, Center for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore, (July 2011-till date).
  • "Successfully defended my Ph.D. Thesis titled, "Significance of Incorporating Chrominance Information for Scene Classification" on Jan 2018 under the supervision of Dr. D. Govind, Asisstant Professor (SG), CEN, and Dr. K. P. Soman, Professor & Head, CEN, Amrita School of Engineering, Coimbatore.
  • Promoted as Assistant Professor (Senior Grade) on Nov, 2015.

Research Area

  • Color Image Processing
  • Hyperspectral Image Processing
  • Pattern Classification
  • Machine Learning
  • Deep Learning
  • Bio-Medical Signal Processing
  • Bio-Medical Image Processing
  • Image Analysis using Drones

Professional Activities

  • Actively Participated in the "Accenture Learning Symposium workshops on Deep Learning and DevOps", conducted on March 13-14, 2018 at Amrita School of Engineering, Coimbatore.
  • Successfully cleared the assessment test and completed a hands on workshop on Artificial Intelligence and Deep Learning, held at Kongu Engineering College, Erode from July 28 - 30, 2018, conducted by leadingIndia.ai, a nation wide initiative by Bennett University, Greater Noida, India. (Listed as one of the toppers in the assessment)
  • Presented a paper tiled, "Inspiring stories from Indian Freedom Movement" in SADGAMAYA 5119, Cultural Camp organized by Amrita Vishwa Vidyapeetham, Coimbatore on 29-30 June 2017.
  • Event Coordinator - ‘Cook without Fire or Wire’ of Amrita Cultural Fest “Amritotsavam-2015’.
  • Participated in one day Seminar on “Projects in Signal & Image Processing, Communication, embedded, Robotics, Networks and VLSI”, organized by Department of Electronics and Communication Engineering, SNS College of Engineering, Coimbatore on 20th July, 2013.
  • Participated in Faculty Development programme on “Linear Algebra and Applications” organized by the department of Mathematics and Centre for Continuing Education held at National Institute of Technology, Calicut during 07-13 July 2013.
  • Participated in National Workshop on “Computer Vision and Image Processing Techniques” organized by Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham, Coimbatore on 15-16 March 2013.
  • Participated in two days workshop on “Geospatial Technologies for Coastal Resources Management”, organized by Department of Earth and Space Sciences, Indian Institute of Space Science and Technology (IIST), Thiruvananthapuram on 19-20 May 2012.
  • Participated in two days workshop on “Machine Vision and Image Processing using Labview”, organized by Department of Instrumentation and Control Systems Engineering, PSG College of Technology, in association with NI Systems (India) Pvt.Ltd, Bangalore on 18-19 May 2012.
  • Participated in “Second Edition of Amrita International Conference of Women in Computing” organized by Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham, Coimbatore on 9-11 January 2012.
  • Co-ordinator of the “First National Workshop on “Sparse Image and Signal Processing (SISP-2011)”, organized by the Centre for Excellence in Computational Engineering and Networking (CEN), Amrita Vishwa Vidyapeetham, Coimbatore during 23rd Dec-26th Dec,2011.

Presentations

  • Presented two papers entitled “Significance of Contrast and Structure Features for an Improved Color Image Classification System”, Improved Color Scene Classification System using Deep Belief Networks and Support vector Machines”, in 2017 International Conference on Signal and Image Processing, ICSIPA-2017, Kuching, Malaysia, 12-14 Sep.2017.
  • Presented a paper entitled ““Edge Detection Using Sparse Banded Filter Matrices” – Second International Symposium on Computer Vision and the Internet (VisionNet’15) held at SCMS School of Engineering, Aluva, Kochi on Aug 10-13, 2015. Publisher: Elsevier Procedia Computer Science Journal. (Published)
  • Presented a paper entitled “Role of Teachers in Nation Building” in the seminar for College Faculty on Swami Vivekananda’s thoughts in the modern context organized by Swami Vivekananda 150th Birth Anniversary Celebration Committee, Coimbatore Region in association with Hindusthan Arts and Science, Coimbatore on September 7, 2013.
  • Rendered a lecture on “Signal and Image Processing Application” on two days workshop on Sparse Image and Signal Processing-2013 organized by organized by the Centre for Excellence in Computational Engineering and Networking (CEN), Amrita Vishwa Vidyapeetham, Coimbatore.
  • Rendered a talk on “Hyperspectral Image Processing” at “First National Workshop on Sparse Image and Signal Processing (SISP-2011), organized by the Centre for Excellence in Computational Engineering and Networking (CEN), Amrita Vishwa Vidyapeetham, Coimbatore.
  • Presented a paper entitled “A Decision Tree Based Land Cover Image Classification Using Color Space and Texture” in 2011 IEEE International Conference on Computational Intelligence and Computing Research, at Cape Institute of Technology, Levengipuram, Kanyakumari, India.
  • Presented a paper entitled “An Effective pre-processing algorithm for detecting noisy spectral bands in hyperspectral imagery” in International Symposium on Ocean Electronics, SYMPOL 2011 at Cochin university of Science and Technology (CUSAT), Kochi.
  • Presented a paper entitled “A Decision Tree Based Land Cover Image Classification Using Color Space and Texture” in National Level Seminar on Signal Processing held at Sree Narayana Guru Institute of Science and technology, Kochi.
  • Rendered Invited Guest Lecture on “Remote Sensing and Applications of GIS” at Avinashilingam University, Coimbatore.

Invited Talks

  • Invited talk on "Deep Learning" in Two days IEEE workshop on Machine Learning held at Kalasalingam University on 2-3 Feb, 2018.
  • Invited talk on "Deep Learning for Bio-medical Application" in ICMR Sponsored Seminar on Deep Learning Techniqies and Tools for Medical Application organized by Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi on 17/01/2018.
  • Delivered a lecture on “Deep Learning for Medical Image Processing” in “TEQIP Sponsored Artificial Intelligence for Biomedical Applications” organized by TKM College of Engineering, Kollam, on 14th Dec, 2017.
  • Delivered a lecture on “Drone based Hyperspectral Imaging for Precision Agriculture” in “Refresher Course for Computer Science” organized by Bharathiar University, on 21st Nov, 2017.
  • Delivered a lecture on “Data Mining” for MBA students of Avinashilingam University on 18th March 2017.
  • Delivered a session on “Least square based image processing” as a part of short term training programme on Digital Signal Processing and its Applications held at Govt.Engineering College, Thrissur on 5th Dec, 2016.
  • Rendered hands on training in “Support Vector Machines using Libsvm and Weka” for M.Tech students of the Department of Electronics and Communication Engineering, Rajiv Gandhi Institute of Technology, Kottayam on 18-12-2015.
  • Delivered one day session on ‘PDE and Image Processing” in two days “National Level Workshop on Signal and Image Processing” conducted by Department of Information Technology, Sona College of technology, Salem during 4-5 Dec, 2015.
  • Rendered a lecture on “PDE based Image Processing” in “A training workshop on Advanced Optimization, Deep Learning Applications (AODA)” organized by organized by the Centre for Excellence in Computational Engineering and Networking (CEN), Amrita Vishwa Vidyapeetham (University), Coimbatore, during January 30 – February 2, 2014.
  • Rendered Invited Guest Lecture on “Remote Sensing and Applications of GIS” at Avinashilingam University, Coimbatore, 2012.
  • Rendered a talk on “Hyperspectral Image Processing” at “First National Workshop on “Sparse Image and Signal Processing (SISP-2011)”, organized by the Centre for Excellence in Computational Engineering and Networking (CEN), Amrita Vishwa Vidyapeetham (University), Coimbatore.

Awards

  • Awarded with "Excellence in Research for the academic year 2016-2017" by Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham on 23rd Institution day held on 10th January 2018.
  • Awarded with "Best Young Researcher Award" rendered by Integrated Intelligent Research Groups on the occasion of Republic Day Achievers award 2018, celebrated at Loyola-ICAM College of Engineering and Technology (LICET), Chennai.
  • Awarded with PG Merit Scholarship for University Rank Holders - Rs.40000 by University Grants Commission (UGC), New-Delhi in the year 2008.
  • Awarded with “Young Women Educator and Researcher” by National Foundation for Entrepreneurship Development (NFED), Coimbatore on 4th Women’s Day Awards celebrated on 8th March 2017 at Grand Reagent Coimbatore.
  • Awarded with “Young Women Achiever (in recognition of Your Role, Outstanding Contributions, Significant Achievements and Sustained Excellence in the field of Engineering) of the Women Awards - VIWA 2016” celebrated on 5 March 2016 at Radha Regent Chennai.
  • Received third prize in Essay Competition conducted by Amrita nature club on the occasion of International Women’s Day on March 8th, 2014.
  • Awarded with title “Associate of the month of January 2011” for project excellence by Cognizant Technology Solutions (CTS), Chennai.
  • Awarded with shield for securing University First Rank in M.TECH (Remote Sensing and Wireless Sensor Networks) (2008-2010) by Amrita Vishwa Vidyapeetham, Coimbatore.
  • Awarded with Gold Medal for University First Rank in B.Sc., (Physics) (2003-2006) by Avinashilingam University, Coimbatore.
  • Awarded with cash award of Rs.5000 for securing First Place in the paper presented in National Level Seminar on Signal Processing held at Sree NarayanaGuru Institute of Science and technology, Kochi.
  • Awarded with “Swami Vivekananda Award” for Excellence in Education by Yuva Kendra Association, Madurai.

Achievements

  • Awarded with Cash Prize of Rs.2000 (each for a Semester) for securing First Rank in M. Tech. (I- IV Semester) Examination by Amrita Vishwa Vidyapeetham, Coimbatore.
  • Proficiency Holder in M.Sc (2006- 2008)
  • Short listed for the National level presentation in “Einstein’s Year of Physics - 2005” conducted by the members of “Indian Association of Physics-Mumbai”.
  • Published an article in Tamil in Amrita University Magazine, Amritadhwani 2013.

Social Activities

  • Active National Service Scheme (NSS) volunteer during the period 2003- 2006.
  • Actively participated in ten day Special Camping Programme organized at a village by Avinashilingam University Coimbatore.
  • Actively participated in three days Residential Youth Camp on Achieving Human Excellence organized by Ramakrishna Mission Vidhyalaya, Coimbatore.
  • Actively participated in Resources Mobilization for Leprosy eradication.
  • Actively served as volunteer on the occasion of the 59th and 60th birthday celebrations of Sri Mata Amnritanandamayi Devi during 26-27 September, 2012,2013.

Guided M. Tech. Dissertations

Year M. Tech. Dissertations Title
2012 Spatial preprocessing for Improved Sparsity based Hyperspectral Image Classification
2014 Classification of Hyperspectral Image using Scattering Transform
2014 Image Classification using Deep Learning Features
2015 Impact of Various Denoising Techniques on Hyperspectral Image Analysis
2015 Hyperspectral Image Classification using  Kernel Methods
2015 Comparative Analysis of Variational Mode & Empirical Mode Features on Hyperspectral Image Classification
2015 Performance Enhancement of Minimum Volume based Hyperspectral Unmixing Algorithm by Empirical Wavelet Transform and Variational Mode Decomposition
2015 Image Fusion using Empirical Wavelet Transform
2015 Enhanced Features for Hyperspectral Image Classification
2016 Impact of Denoising and Dimensionality Reduction Technique on Kernel Based Hyperspectral Image Classification
2016 Decomposition Techniques Applied for Pan Sharpening
2017 Color image Dehazing using Variational Mode Decomposition
2017 Scene classification using Deep Belief Network and Support Vector Machine
2017 Effect of denoising and dimensoinality reduction on Vectorized Convolutional Neural Network for Hyperspectral Image Classification
2017 Effect of Dynamic Mode Decomposition based dimension reduction technique on Hyperspectral Image Classification
2017 Dependency of various color and intensity planes on CNN based image classification
2017 Effect of denoising and Variational Mode Decomposition based dimensionality reduction on sparsity based hyperspectral unmixing
2017 Fusion of panchromatic image with low-resolution multi-spectral images using NIHS Transform and Decomposition techniques
2018 Single Plane Scene Image Classification Using Deep Convolution Features
2018 Convolution and Recurrent Neural Networks for Disease Diagnosis
2018 Deep Learning Architecture for Land Cover Classification Using Red and Near-Infrared Satellite Images
2018 Dimensionality Reduction for Hyperspectral Image Classification Using Deep Learning and Kernel Methods
2018 Randomized Methods for Dimensionality Reduction of Hyperspectral Image Classification
2018 Effect of Denoising on Hyperspectral Image Classification Using Deep Networks and Kernel Methods
2018 Spatio-Spectral Compression and Analysis of Hyperspectral Images Using Tensor Decomposition Techniques
2018 Deep Rectified System for High-Speed Tracking in Images (DRSHTI)

Publications

Publication Type: Journal Article

Year of Conference Publication Type Title

2018

Journal Article

V. Ankarao, Sowmya V., and Dr. Soman K. P., “Multi-sensor data fusion using NIHS transform and decomposition algorithms”, Multimedia Tools and Applications, 2018.[Abstract]


Multi-spectral image fusion is to enhance the details present in multi-spectral bands with the spatial information available in the panchromatic image. Fused images have the effect of spectral distortions and lack of structural similarity. To overcome these limitations, three methods are proposed using intensity, hue, saturation (IHS) and nonlinear IHS (NIHS) transform along with the Dynamic Mode Decomposition (DMD) and 2D-Empirical Mode Decomposition (2D-EMD or IEMD). An intensity plane is calculated from the NIHS transform. The modes are constructed using DMD by considering the variations between the intensity plane computed using NIHS transforms of a low resolution multi-spectral image and a panchromatic image. Similarly, 2D-EMD is also used for image fusion. Modes are subjected to weighted fusion rule to get an intensity plane with spatial and edge information. Finally, the calculated intensity plane is concatenated along with the hue and saturation plane of low-resolution multi-spectral image and transformed into RGB color space. Thus, the fused images have high spatial and edge information on spectral bands. The experiments and its quality assessment assure that proposed methods perform better than the existing methods. More »»

2018

Journal Article

M. Swarna, Sowmya V., and Dr. Soman K. P., “Band selection using variational mode decomposition applied in sparsity-based hyperspectral unmixing algorithms”, Signal, Image and Video Processing, 2018.[Abstract]


In this work, a frequency-based dimensionality reduction technique using variational mode decomposition (VMD) is proposed. Dimensionality reduction is a very important aspect of preprocessing in case of hyperspectral image (HSI) analysis where this step helps in elimination of the lesser informative bands, thereby reducing the size of the data and making its processing computationally less challenging. In contrast to the standard dimensionality reduction methods such as inter-band block correlation (IBBC) where bands are eliminated based on their similarity with the consecutive bands, the proposed method uses frequency information of each band to categorize it as a less or more informative band. In this way, only the topmost informative bands of HSI are selected to form the reduced dataset. In our experiment, in order to verify the efficiency of VMD as a dimensionality reduction technique, the hyperspectral unmixed results obtained for IBBC reduced dataset is compared with those obtained for VMD reduced dataset. From the parametric measures such as classification accuracy, root-mean-square error (RMSE) and visual results obtained after unmixing for both IBBC and VMD reduced datasets, it is noticed that the VMD reduced dataset performs better by achieving higher classification accuracy and lower RMSE than that of the existing IBBC method.

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2017

Journal Article

Sowmya V., Govind, D., and Soman, K. P., “Significance of incorporating chrominance information for effective color-to-grayscale image conversion”, Signal, Image and Video Processing, vol. 11, no. 1, pp. 129–136, 2017.[Abstract]


This paper provides an alternative framework for color-to-grayscale image conversion by exploiting the chrominance information present in the color image using singular value decomposition (SVD). In the proposed technique of color-to-grayscale image conversion, a weight matrix corresponds to the chrominance components is derived by reconstructing the chrominance data matrix (planes a* and b*) from the eigenvalues and eigenvectors computed using SVD. The final grayscale converted image is obtained by adding the weighted chrominance data to the luminous intensity which is kept intact for the CIEL*a*b* color space of the given color image. The effectiveness of the proposed grayscale conversion is confirmed by the comparative analysis performed on the color-to-gray benchmark dataset across 10 existing algorithms based on the standard objective measures, namely normalized cross-correlation, color contrast preservation ratio, color content fidelity ratio, E score and subjective evaluation.

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2017

Journal Article

V. V. Pradeep, Sowmya V., and Dr. Soman K. P., “Application of M-band wavelet in pan-sharpening”, Special Issue in the Journal of Intelligent and Fuzzy Systems, IOS Press, Netherlands, vol. 32, no. 4, pp. 3151-3158, 2017.[Abstract]


Remote sensing satellites are proficient in taking earth images across various regions in visible part of electromagnetic spectrum. The images can be panchromatic image of a single band, multispectral image of three to seven different bands, and hyperspectral image taken from about 220 contiguous spectral bands. These images are used together or on its own, depending on the significance and usage of the preferred application. Pan-sharpening is one method which is used to improve the quality of a low resolution multispectral image by fusion with a high resolution panchromatic image. This paper proposes a method based on M-band wavelets for the pan-sharpening of a low resolution multispectral image. The method tries to improve the spatial characteristics while preserving the spectral quality of the data. The proposed technique uses weighted fusion rule and average fusion rule. The data used for the experiment were acquired by high resolution optical imagers onboard QuickBird, WorldView-3, WorldView-2 and GeoEye-1. A comparison with existing fusion techniques is done based on image quality metrics and visual interpretation. The experimental results and analysis suggests that the proposed pan-sharpening technique outperforms other compared pre-existing pan-sharpening methods. © 2017-IOS Press and the authors. All rights reserved.

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2017

Journal Article

Sowmya V., “Effect of Legendre-Fenchel Denoising and SVD based Dimensionality Reduction Algorithm on Hyperspectral Image Classification”, Special Issue in the Journal of Neural Computing and Applications, Springer, 2017.

2017

Journal Article

Sowmya V., Dr. Govind D., and Soman, K. Padanyl, “Significance of perceptually relevant image decolorization for scene classification”,  Journal of Electronic Imaging, 2017.[Abstract]


A color image contains luminance and chrominance components representing the intensity and color information respectively. The objective of the work presented in this paper is to show the significance of incorporating the chrominance information for the task of scene classification. An improved color-to-grayscale image conversion algorithm by effectively incorporating the chrominance information is proposed using color-to-gay structure similarity index (C2G-SSIM) and singular value decomposition (SVD) to improve the perceptual quality of the converted grayscale images. The experimental result analysis based on the image quality assessment for image decolorization called C2G-SSIM and success rate (Cadik and COLOR250 datasets) shows that the proposed image decolorization technique performs better than 8 existing benchmark algorithms for image decolorization. In the second part of the paper, the effectiveness of incorporating the chrominance component in scene classification task is demonstrated using the deep belief network (DBN) based image classification system developed using dense scale invariant feature transform (SIFT) as features. The levels of chrominance information incorporated by the proposed image decolorization technique is confirmed by the improvement in the overall scene classification accuracy . Also, the overall scene classification performance is improved by the combination of models obtained using the proposed and the conventional decolorization methods. More »»

2016

Journal Article

L. S. Kiran, Sowmya V., and Dr. Soman K. P., “Enhanced Variational Mode Features for Hyperspectral Image Classification”, Journal of Chemical and Pharmaceutical Sciences, vol. 9, pp. 502-505, 2016.[Abstract]


Variational Mode Decomposition (VMD) is a recent method and is gaining popularity in the area of signal and image processing. The use of this decomposition technique in hyper spectral image classification is discussed in detail in this paper. The role of VMD as a feature extraction technique is exploited here. The proposed method includes an initial stage of dimensionality reduction so as to reduce the computational complexity. A final stage of recursive filtering is also added to further enhance the results. Results obtained by the proposed method on two hyper spectral image datasets 'Indian Pines and Salinas-A, suggests that VMD is a promising method in the area of image analysis and classification. Quality indices used for experimental analysis include overall accuracy (OA), average accuracy (AA) and kappa coefficient. Notable classification accuracy has been obtained for both the datasets and a final stage of recursive filtering has further improved the results (more than 98% accuracy in the case of Indian Pines). More »»

2016

Journal Article

S. Se, Pradeep, D., Sowmya V., and Dr. Soman K. P., “Fourier Descriptor features for Shape Deformation Classification using Random Kitchen Sink”, Journal of Chemical and Pharmaceutical Sciences, vol. 9, pp. 554-558, 2016.[Abstract]


This paper deals with the Fourier descriptor features for shape deformation classification using Random Kitchen Sink algorithm accessed through GURLS library. Shape recognition is an important method used in all industrial environments which are mostly concerned with robots. It is a highly essential task to make the robot understand the shape of an object. The object may have many deformed shapes and so it is necessary to train the classifier accordingly. Recognition methods based on polar coordinates and probabilistic models are already developed, but its accuracy for finding the deformed shape of the object is low. In this context, Random Kitchen Sink algorithm is used and the classification is done through GURLS in which, regularized least square method is used, which leads to better shape recognition. More »»

2016

Journal Article

N. Nechikkat, Sowmya V., and Dr. Soman K. P., “Low dimensional variational mode features for hyperspectral image classification”, Journal of Chemical and Pharmaceutical Sciences, vol. 9, pp. 565-570, 2016.[Abstract]


High Dimensionality is always a great concern while working with hyperspectral images. The high dimension of hyperspectral image increases the computational complexity, creates data storage issues and decrease the performance and accuracy of hyperspectral image analysis algorithms. This paper focuses on low dimensional Variational Mode features for hyperspectral image classification. The proposed method consist of three stages: preprocessing using Inter Band Block Correlation (IBBC) technique, feature extraction using Variational Mode Decomposition (VMD) and dimensionality reduction using Singular Value Decomposition (SVD). The efficiency of the proposed method based on the low dimensional feature extraction using VMD is evaluated by one of the sparsity based classification algorithms namely Orthogonal Matching Pursuit (OMP). The proposed work is experimented on the standard dataset namely Indian pines acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The experimental analysis shows that our proposed technique produces 90.88% overall accuracy with 40% of training which is greater than the classification accuracy obtained without feature extraction. More »»

2016

Journal Article

Reshma R, Sowmya V., and Dr. Soman K. P., “Improvement in kernel based Hyperspectral image classification using legendre fenchel denoising”, Indian Journal of Science and Technology, vol. 9, no. 33, 2016.[Abstract]


Hyperspectral images have bulk of information which are widely used in the field of remote sensing. One of the main problems faced by these images is noise. This emphasizes the importance of denoising techniques for enhancing the image quality. In this paper, Legendre Fenchel Transformation (LFT) is used for preprocessing the Indian Pines Dataset. LFT reduces the noise of each band of the hyperspectral image without affecting the edge information. Signal to noise ratio is computed which helps to evaluate the performance of denoising. Further, the denoised image is classified using GURLS and LibSVM and the various accuracies are estimated. The experimental analysis shows that the overall and classwise accuracies are more for the preprocessed data classification when compared to the classification without preprocessing. The classification accuracy is improved with denoising of hyperspectral image.

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2016

Journal Article

V. V. Pradeep, Sowmya V., and Soman, K. P., “Variational mode decomposition based multispectral and panchromatic image fusion”, International Journal of Control Theory and Applications, vol. 9, no. 16, pp. 8051-8059, 2016.[Abstract]


The technique of fusing multispectral image with panchromatic image in order to get a resultant output image of relatively higher spectral resolution and higher spatial information is termed as pan sharpening. It is being used in many remote sensing tasks for different applications including classification, segmentation, change detection, etc. This paper proposes the usage of Variational Mode Decomposition (VMD) as a technique for fusing multispectral and panchromatic images. It also considers average fusion rule and weighing fusion rule during its procedural steps. The experiment is being done on datasets acquired by high resolution sensors on-board satellites such as QuickBird, WorldView-3, WorldView-2 and GeoEye-1. Quantitative assessment measures and visual perception evaluates the effectiveness of the method. The analysis from the obtained results suggest that the proposed method can be used as an image fusion technique and its performance is comparable to the pre-existing pan sharpening techniques like Multi-resolution Singular Value Decomposition (MSVD), Discrete Wavelet Transform (DWT) and Empirical Wavelet Transform (EWT). © International Science Press.

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2016

Journal Article

D. Merlin K. Dixon, Ajay, A., Soman, K. P., and Sowmya V., “Aerial and satellite image denoising using least square weighted regularization method”, Indian Journal of Science and Technology, vol. 9, no. 30, 2016.[Abstract]


Remotely sensed images are subjected to various types of noises. Noise interrupts the image information; hence noise removal is one of the important pre-processing steps in every image processing applications. Since both noise and edges contain highintensity values, image denoising leads to smoothening of the edges thereby reducing the visual quality of the image. Hence, edge preserved image denoising is an ever-relevant topic. Over decades, several image denoising techniques were developed. Most of the denoising algorithms are very complex and time consuming. Background/Objectives: This paper introduces a novel image denoising technique based on least square weighted regularization. Methods/Statistical Analysis: The onedimensional signal denoising introduced by14 is mapped into two-dimensional image denoising. The proposed method is experimented on a set of colored aerial and satellite images. The column-wise denoising of the image is performed first, followed by row-wise denoising. The performance of the proposed method is evaluated based on the standard quality metric peak signal-to-noise ratio and computational time. Findings: From the experimental results, it is observed that the proposed method outperforms the earlier denoising methods on the basis of time and complexity. Applications/Improvements: The proposed denoising technique can be adopted as a faster pre-processing step in most of the image processing applications.

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2016

Journal Article

V. P.V, G, R. Devi, Sowmya V., and Dr. Soman K. P., “Least Square based image denoising using wavelet filters”, Indian Journal of Science and Technology, vol. 9, no. 30, 2016.[Abstract]


Background/Objectives: Noise in a digital image, is unwanted information that degrades the quality of an image. The main aim of the proposed method is to denoise a noisy image based on least square approach using wavelet filters. Methods/ Statistical Analysis: One dimensional least square approach proposed by Selesnick is extended to two dimensional image denoising. In our proposed technique of least square problem formulation for image denoising, the matrix constructed using second order filter coefficients is replaced by wavelet filter coefficients. Findings: The method is experimented on standard digital images namely Lena, Cameraman, Barbara, Peppers and House. The images are subjected to different noise types such as Gaussian, Salt and Pepper and Speckle with varying noise level ranging from 0.01db to 0.5db. The wavelet filters used in the proposed approach of denoising are Haar, Daubechies, Symlet, Coiflet, Biorthogonal and Reverse biorthogonal. The outcome of the experiment is evaluated in terms of Peak Signal to Noise Ratio (PSNR). The analysis of the experiment results reveals that performance of the proposed method of least square based image denoising by wavelet filters are comparable to denoising using existing second order sparse matrix. Applications/Improvements: Digital images are often prone to noise; hence, proceeding with further processing of such an image requires denoising. This work can be extended in future to m-band wavelet filters.

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2016

Journal Article

H. T. Suseelan, Sudhakaran, S., Sowmya V., and Dr. Soman K. P., “Performance Evaluation of Sparse Banded Filter Matrices using content based image retrieval”, Institute of Integrative Omics and Applied Biotechnology, vol. 7, no. 3, pp. 11-18, 2016.[Abstract]


Content Based Image Retrieval (CBIR) is an extensively used application in the field of Image Processing. It is used to search through a massive database and retrieve the images that have similarity with the query image. In this paper, performance is evaluated for Sparse Banded Filter matrices (ABfilter) against the standard edge detection filters through Content Based Image Retrieval. Performance factor of ABfilter directly relates to its edge detection capabilities. Here, edge detection followed by the Singular Value Decomposition (SVD) is done for feature extraction for both the query and images in database. Query image feature and database image features are matched and those having similar values are retrieved. Similarity measurement is done by computing the distance between corresponding features. Experimental results indicate that retrieval results using ABfilter is much better than using standard edge detection filters for the same, which in turn establishes its superiority in edge detection.

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2016

Journal Article

A. M, N, D., Sowmya V., Mahan, N., and Dr. Soman K. P., “Least Square Based approach for Image Inpainting”, Institute of Integrative Omics and Applied Biotechnology, vol. 7, pp. 44-59, 2016.[Abstract]


Images are widely used over various applications under the aegis of various domains like Computer vision, Biomedical, etc. The problem of missing data identification is of great concern in various fields involving image processing. Least square can be used for missing sample estimation for 1-D signals. The proposed system extends the missing sample estimation in 1-D using least square to 2-D, applied for image inpainting. The paper also draws a comparison between the Total Variation (TV) algorithm and the proposed method. The experiments were conducted on standard images and the standard metrics namely PSNR and SSIM are used to compare the image quality obtained using the proposed method (least square based) and TV algorithm. More »»

2016

Journal Article

D. P. Kuttichira, Sowmya V., and Dr. Soman K. P., “Digit recognition using multiple feature extraction”, IIOAB Journal, vol. 7, pp. 37-43, 2016.[Abstract]


Digit Recognition is one of the classic problems in pattern classification. It has ten labels which are digits from 0-9 and each prototypes in the test set has to be classified under these labels. In this paper, we have used MNIST data for training and testing. MNIST database is a standard database for digit classification. A number of neural network algorithms have been used on MNIST to get high accuracy outputs. These algorithms are computationally costly. Here, we have used multiple feature extraction based on SVD and histogram to create testing and training matrix. To the feature vector formed by SVD, histogram values along x-axis and y-axis of an image is appended. These vectors are mapped to hyperplane using polynomial and Gaussian kernel. For classification open source software like GURLS and LIBSVM is used to obtain a fairly good accuracy. © 2016, Institute of Integrative Omics and Applied Biotechnology. All rights reserved.

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2016

Journal Article

Sowmya V., Praveena, R., and K. P. Soman, “Least Square based Signal Denoising and Deconvolution using Wavelet Filters”, Indian Journal of Science and Technology, vol. 9, no. 33, 2016.[Abstract]


Noise, the unwanted information in a signal reduces the quality of signal. Hence to improve the signal quality, denoising is done. The main aim of the proposed method in this paper is to deconvolve and denoise a noisy signal by least square approach using wavelet filters. In this paper, least square approach given by Selesnick is modified by using different wavelet filters in place of second order sparse matrix applied for deconvolution and smoothing. The wavelet filters used in the proposed approach for denoising are Haar, Daubechies, Symlet, Coiflet, Biorthogonal and Reverse biorthogonal. The result of the proposed experiment is validated in terms of Peak Signal to Noise Ratio (PSNR). Analysis of the experiment results notify that proposed denoising based on least square using wavelet filters are comparable to the performances given by deconvolution and smoothing using the existing second order filter.

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2016

Journal Article

Sowmya V., Divu, G., and Dr. Soman K. P., “Significance of incorporating chrominance information for effective color-to-grayscale image conversion”, Signal, Image and Video Processing, vol. 11, 2016.[Abstract]


This paper provides an alternative framework for color-to-grayscale image conversion by exploiting the chrominance information present in the color image using singular value decomposition (SVD). In the proposed technique of color-to-grayscale image conversion, a weight matrix corresponds to the chrominance components is derived by reconstructing the chrominance data matrix (planes a* and b*) from the eigenvalues and eigenvectors computed using SVD. The final grayscale converted image is obtained by adding the weighted chrominance data to the luminous intensity which is kept intact for the CIEL*a*b* color space of the given color image. The effectiveness of the proposed grayscale conversion is confirmed by the comparative analysis performed on the color-to-gray benchmark dataset across 10 existing algorithms based on the standard objective measures, namely normalized cross-correlation, color contrast preservation ratio, color content fidelity ratio, E score and subjective evaluation. More »»

2015

Journal Article

M. Aswathi, M. Babu, J., Sowmya V., and Soman, K. P., “Smart meter data security based on cosine transform and chaotic theory”, International Journal of Applied Engineering Research, vol. 10, no. 20, pp. 19181-19185, 2015.[Abstract]


The advent of Smart Metering has facilitated the knowledge of fine-grained energy-consumption data. The data is transmitted over the internet which is prone to access by an undesired third party. The high resolution data gives information regarding the amount of electricity and the type of appliances used. Smart meters have also become potential surveillance devices that monitor the behavior of the customers. This has paved way for the need for powerful privacy preservation techniques. To ensure security, we introduce a technique of obfuscating the data before transmission by computing the DCT of the signal and adding a chaotic signal to it. The proposed approach is experimented on real household’s power signal data. © Research India Publications.

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2015

Journal Article

S. Moushmi, Sowmya V., and Soman, K. P., “Multispectral and Panchromatic Image Fusion using Empirical Wavelet Transform”, Indian Journal of Science and Technology, vol. 8, no. 24, 2015.[Abstract]


Pan sharpening is the process of fusion of panchromatic and multispectral image to obtain an output image of high spatial and spectral resolution. It is very important for various remote sensing applications such as image segmentation studies, image classification, temporal change detection etc. The present work demonstrates the application of Empirical Wavelet Transform for the fusion of panchromatic image and multispectral image by simple average fusion rule. The Proposed method is experimented on panchromatic and multispectral images captured by high resolution earth observation satellites such as GeoEye-1, QuickBird, WorldView-2 and World View-3. The effectiveness of our proposed method is evaluated by visual perception and quantitative assessment measures. The experimental analysis shows that the proposed method performs comparable to the existing fusion algorithms such as Multi-resolution Singular Value Decomposition and Discrete Wavelet Transform.

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2015

Journal Article

P. G. Mol, Sowmya V., and Soman, K. P., “Performance Enhancement of Minimum Volume based Hyper Spectral Unmixing Algorithms by Variational Mode Decomposition”, Indian Journal of Science and Technology, vol. 8, no. 24, 2015.[Abstract]


Hyper spectral unmixing of data has become an indispensable technique in remote sensing zone. Spectral Unmixing is defined as the source separation of a mixed pixel. The fundamental sources are termed as endmembers and percentage of the source content is known as abundances. This paper demonstrates the effect of Variational Mode Decomposition (VMD) on hyper spectral unmixing algorithms based on geometrical minimum volume approaches. The proposed method is experimented on standard hyper spectral dataset namely, cuprite. The effectiveness of the proposed method is subjected to evaluation, based on the standard quality metric namely, Root Mean Square Error (RMSE). The experimental result analysis shows that, the proposed technique enhance the performance of hyper spectral unmixing algorithms based on the geometrical minimum volume based approaches.

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2015

Journal Article

N. Haridas, Sowmya V., and Soman, K. P., “GURLS vs LIBSVM: Performance Comparison of Kernel Methods for Hyperspectral Image Classification”, Indian Journal of Science and Technology, vol. 8, no. 24, 2015.[Abstract]


Kernel based methods have emerged as one of the most promising techniques for Hyper Spectral Image classification and has attracted extensive research efforts in recent years. This paper introduces a new kernel based framework for Hyper Spectral Image (HSI) classification using Grand Unified Regularized Least Squares (GURLS) library. The proposed work compares the performance of different kernel methods available in GURLS package with the library for Support Vector Machines namely, LIBSVM. The assessment is based on HSI classification accuracy measures and computation time. The experiment is performed on two standard Hyper Spectral datasets namely, Salinas A and Indian Pines subset captured by AVIRIS (Airborne Visible Infrared Imaging Spectrometer) sensor. From the analysis, it is observed that GURLS library is competitive to LIBSVM in terms of its prediction accuracy whereas computation time seems to favor LIBSVM. The major advantage of GURLS toolbox over LIBSVM is its simplicity, ease of use, automatic parameter selection and fast training and tuning of multi-class classifier. Moreover, GURLS package is provided with an implementation of Random Kitchen Sink algorithm, which can easily handle high dimensional Hyper Spectral Images at much lower computational cost than LIBSVM.

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2015

Journal Article

A. C, Haridas, N., Sowmya V., and Dr. Soman K. P., “Effect of AB filter denoising on ADMM based Hyperspectral Image Classification”, International Journal of Applied Engineering Research (IJAER), vol. 10, no. 73, pp. 127-131, 2015.[Abstract]


In recent years, hyperspectral remote sensing has emerged as a prominent area of research. This has developed a lot of practical solutions to solve the various challenges faced in the field. Noise is one of such issues which deteriorate the quality of information present in the hyperspectral images. In order to address this problem, various preprocessing (denoising) techniques are applied prior to data analysis. In this paper, the proposed method evaluates the effect of Hyperspectral Image (HSI) denoising employing AB filter on optimization based classification which uses Basis Pursuit solved by Alternating Direction Method of Multipliers (ADMM). AVIRIS Indian Pines dataset is used for the experimental study. The efficiency of the proposed technique is proved by a comparative study with other existing preprocessing methods. The experimental result analysis based on visual interpretation and quantitative assessment shows that the proposed method provides better classification results compared to the existing methods. The classification results are assessed by Overall Accuracy, Average accuracy and Kappa coefficient.

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2015

Journal Article

N. Nechikkat, Sowmya V., and Soman, K. P., “A Comparative Analysis of Variational Mode and Empirical Mode Features on Hyperspectral Image Classification”, International Journal of Applied Engineering Research, vol. 10, no. 73, 2015.[Abstract]


Considering the fact that involving spatial information in feature extraction significantly improves the classification accuracies, this paper focuses on Variational Mode Decomposition (VMD) and Empirical Mode Decomposition (EMD) as the featureextraction algorithms. Both the algorithms decompose an input image into different modes with each mode including different regions of frequency with unique properties. Here, the proposed method includes processing the same set of data with two different decomposition methods to compare the effect of the methods on the standard dataset. The method incorporates a preprocessing technique for noisy band removal, processing technique for feature extraction, band selection methods for dimensionality reduction and classification technique for result validation

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2015

Journal Article

Sowmya V., S., M., and Soman, K. P., “Performance Comparison of Empirical wavelet Transform and Empirical Mode Decomposition on Pan sharpening”, International Journal of Applied Engineering Research, vol. 10, no. 73, 2015.[Abstract]


Pan sharpening is defined as the fusion of low resolution multispectral image with panchromatic image, which plays a significant role in the field of remote sensing. This work considers the fusion of multispectral and panchromatic images (Pan sharpening) using EWT (Empirical Wavelet Transform) and EMD (Empirical Mode Decomposition). EWT and EMD algorithm decomposes the input image into several modes. This paper focus on the performance evaluation of image fusion technique based on EMD and EWT. The image fusion methods based on EWT and EMD are experimented on five sets of panchromatic and multispectral images captured by high resolution earth observation satellites. The efficiency of the fusion methods are evaluated by visual perception and standard fusion metrics. The experimental result analysis based on computed fusion metrics and computation time shows that the fusion method based on EWT outperforms the EMD based fusion approach.

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2014

Journal Article

P. S. Ashitha, Sowmya V., and Soman, K. P., “Classification of hyperspectral images using scattering transform”, International Journal of Scientific & Engineering Research, vol. 5, no. 7, pp. 315-319, 2014.[Abstract]


In this paper, we applied scattering transform approach for the classification of hyperspectral images. This method integrates features, such as the translational and rotational invariance features for image classification. The classification of hyperspectral images is more challenging because of the very high dimensionality of the pixels and the small number of labelled examples typically available for learning. The scattering transform technique is validated with two standard hyperspectral datasets i.e, SalinasA_Scene and Salinas_Scene. The experimental result analysis proves that the applied scattering transform method provides high classification accuracy of 99.35% and 89.30% and kappa coefficients of 0.99 and 0.88 for the mentioned hyperspectral image dataset respectively.

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2014

Journal Article

Sowmya V., P., S. K., and Deepika, J., “Image Classification Using Convolutional Neural Networks”, International Journal of Scientific & Engineering Research , vol. 5, no. 6, p. 06/2014, 2014.[Abstract]


Convolutional Neural Networks (CNNs) have been established as a powerful class of models for image recognition problems. Inspired by a blog post [1], we tried to predict the probability of an image getting a high number of likes on Instagram. We modified a pre-trained AlexNet ImageNet CNN model using Caffe on a new dataset of Instagram images with hashtag ‘me’ to predict the likability of photos. We achieved a cross validation accuracy of 60% and a test accuracy of 57% using different approaches. Even though this task is difficult because of the inherent noise in the data, we were able to train the model to identify certain characteristics of photos which result in more likes.

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2012

Journal Article

K. P. Soman, Kumar, S., Sowmya V., and Shajeesh, K. U., “Computational Thinking with Spreadsheet: Convolution, High-Precision Computing and Filtering of Signals and Images”, International Journal of Computer Applications, vol. 60, pp. 1-7, 2012.[Abstract]


Modern day innovations in sciences and engineering are direct outcome of human’s capacity for abstract thinking thereby creating effective computational models of the problems that can be solved efficiently by the number crunching and massive data handling capabilities of modern networked computers. Survival of any economy now depends on innovating-capacity of its citizens. Thus capacity for computational thinking has become an essential skill for survival in the 21st century. It is necessitating a fundamental change in our curriculum in schools. Computational thinking need to be introduced incrementally along with standard content in a way that makes the standard content easier to learn and vice versa. When learners successfully combine disciplinary knowledge and computational methods they develop their identity as Computational Thinkers. The need for trainers, training content and training methodology for imparting computational thinking has become subject of discussion in many international forums. In this article the use of spreadsheet as a tool for developing computational-thinking -capabilities by integrating it with existing curricula is explored. Concept of convolution which everybody uses when one does any multiplication is taken as a vehicle to develop exercises that enhance computational thinking. It is shown how convolution is visualized and implemented and also discussed a wide variety of computational experiments that students at various levels can do with the help of spreadsheet

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2012

Journal Article

K. Balakrishnan, Soman, K. P., and Sowmya V., “Spatial preprocessing for improved sparsity based hyperspectral image classification”, International Journal of Engineering Research and Technology (IJERT), vol. 1, no. 5, 2012.[Abstract]


In this paper, we present that hyperspectral image classification based on sparse representation can be significantly improved by using an image enhancement step. Spatial enhancement allows further analysis of hyperspectral imagery, as it reduces the intensity variations within the image. Perona-Malik, a partial differential equation based non-linear diffusion scheme is used for the enhancement of the hyperspectral imagery prior to classification. The diffusion technique applied smoothens the homogenous areas of hyperspectral imagery and thereby increases the separability of the classes. The diffusion scheme is applied individually to each band of the hyperspectral imagery and it does not take into account the spectral relationship among different bands. Experiments are performed on the real hyperspectral dataset AVIRIS (Airborne Visible/IR Imaging Spectrometer) 1992 Indiana Indian Pines imagery. We compared the classification statistics of hyperspectral imagery before and after performing the spatial preprocessing step in order to prove the effectiveness of the proposed method. The experiments results proved that the hyperspectral image classification using sparse representation along with spatial enhancement step lead to 97.53% of classification accuracy which is high when compared with the classification accuracy obtained without applying the spatial preprocessing technique.

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Publication Type: Conference Proceedings

Year of Conference Publication Type Title

2017

Conference Proceedings

Sowmya V., V. Ankarao, and K. P. Soman, “Fusion of panchromatic image with low-resolution multispectral images using Dynamic Mode Decomposition”, International Conference on Nextgen Electronic Technologies: Silicon to Software (ICNETS2). (LNEE Springer Proceedings), VIT University, Chennai Campus, India, pp. 23-25 , 2017.

2017

Conference Proceedings

Sowmya V., Megha .P, and K. P. Soman, “Effect of Dynamic Mode Decomposition Based Dimension Reduction Technique on Hyperspectral Image Classification”, International Conference on Nextgen Electronic Technologies: Silicon to Software (ICNETS2. LNEE Springer Proceedings, VIT University, Chennai Campus, India, pp. 23-25 , 2017.

2017

Conference Proceedings

Sowmya V., Deepa Merlin Dixon.K, and K. P. Soman, “Effect of Denoising on Vectorized Convolutional Neural Network for Hyperspectral Image Classification”, International Conference on Nextgen Electronic Technologies: Silicon to Software (ICNETS2). LNEE Springer Proceedings, VIT University, Chennai Campus, India, pp. 23-25 , 2017.

2017

Conference Proceedings

Sowmya V., Hima . T.Suseelan, and K. P. Soman, “Image Dehazing Using Variational Mode Decomposition”, International Conference on Wireless Communications Signal Processing and Networking (WISPNET). IEEE, SSN College of Engineering, Chennai, India, pp. 22-24 , 2017.

2017

Conference Proceedings

Sowmya V., V. Ankarao, and K. P. Soman, “Sparse Image Denoising using Dictionary constructed based on Least Square Solution”, International Conference on Wireless Communications Signal Processing and Networking (WISPNET). IEEE, SSN College of Engineering, Chennai, India, pp. 22-24 , 2017.

2017

Conference Proceedings

Sowmya V., Aleena Ajay, and K. P. Soman, “Vehicle detection in Aerial imagery using Eigen features”, IEEE International Conference on Communication and Signal Processing-ICCSP'17. Adhiparasakthi Engineering College, Melmaruvathur , pp. 6-8, 2017.

2017

Conference Proceedings

Sowmya V., Ashwini B, Neethu Mohan, Shriya se, and K. P. Soman, “Performance Evaluation of Edge Feature Extracted using Sparse Banded Matrix Filter Applied for Face Recognition”, IEEE International Conference on Circuit, Power and Computing, ICCPCT-2017. Baselious Mathews II College of Engineering, Kerala, pp. 19-20, 2017.

2017

Conference Proceedings

M .Srikanth, K.S. Gokul Krishnan, Sowmya V., and K. P. Soman, “Image Denoising based on Weighted Regularized Least Square Method”, IEEE International Conference on Circuit, Power and Computing, ICCPCT-2017. Baselious Mathews II College of Engineering, Kerala, pp. 19-20, 2017.

2017

Conference Proceedings

Vishnu Pradeep V, Reshma R, Sowmya V., and K. P. Soman, “Comparative Analysis of Sparsity based and Kernel based algorithms for Hyperspectral Image Classification”, IEEE International Conference on Circuit, Power and Computing, ICCPCT-2017. Baselious Mathews II College of Engineering, Kerala, pp. 19-20, 2017.

2017

Conference Proceedings

Swarna .M, Sowmya V., and K. P. Soman, “Effect of Denoising on Dimensionally Reduced Sparse Hyperspectral Unmixing”, 7th International Conference on Advances in Computing and Communications , ICACC-2017. Elsevier, Rajagiri School of Engineering & Technology, pp. 22-24 , 2017.

2017

Conference Proceedings

Chippy Jayaprakash, Naveen Varghese Jacob, Renu .R.K., Sowmya V., and K. P. Soman, “Least Square Denoising in Spectral Domain for Hyperspectral Images”, 7th International Conference on Advances in Computing and Communications , ICACC-2017. Elsevier, Rajagiri School of Engineering & Technology, pp. 22-24 , 2017.

2016

Conference Proceedings

Srivatsa .S, Sowmya V., and K. P. Soman, “Least Square Based Fast Denoising approach to Hyperspectral Imagery”, 4th International Conference on Advanced Computing, Networking and Informatics, ICACNI-2016, Centre for Computer Vision and Pattern Recognition, . NIT-Rourkela, pp. 22 - 24, 2016.

2016

Conference Proceedings

Sreelekshmy Selvin, S .GAjay, B.Ganga Gowri, Sowmya V., and K. P. Soman, “l1 Trend Filter for Image Denoising”, 6th International Conference on Advances in Computing and Communications , ICACC-2016. Rajagiri School of Engineering & Technology, 2016.[Abstract]


The major problem in digital image processing is the presence of unwanted frequencies(noise). In this paper ℓ1 trend filter is proposed as an image denoising technique. ℓ1-trend filter estimates the hidden trend in the data by formulating a convex optimization problem based on ℓ1 norm. The proposed method extends the application of ℓ1 trend filter from one dimensional signals to three dimensional color images. Here the filter is applied over the image in a cascade, initially filtering along the rows followed by filtering along the columns. This identifies the hidden image information from the noisy image resulting in a smooth or denoised image. The proposed method is compared with the wavelet denoising technique using the quality metrics Peak-Signal-to-Noise-Ratio(PSNR) and Structural Similarity Index(SSIM). © 2016 The Authors. Published by Elsevier B.V.

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2016

Conference Proceedings

Swarna .M, Sowmya V., and K. P. Soman, “Effect of Dimensionality Reduction on Sparsity Based Hyperspectral Unmixing”, 8th International Conference on Soft Computing and Pattern Recognition (SoCPAR). School of Information Technology and Engineering, VIT University, Vellore, India, pp. 19-21 , 2016.

2015

Conference Proceedings

C. Aswathy, Sowmya V., Gandhiraj R., and Soman, K. P., “Hyperspectral image denoising using legendre Fenchel Transformation for improved Multinomial Logistic Regression based classification”, 4th IEEE International Conference on Communications and Signal Processing (ICCSP), 2015 . IEEE, Adhiparasakthi Engineering College , Melmaruvathur, pp. 1670 - 1674, 2015.[Abstract]


The abundant spectral and spatial information in the hyperspectral images (HSI) are largely used in the field of remote sensing. Though there are highly sophisticated sensors to capture the hyperspectral imagery, they suffer from issues like hyperspectral noise and spectral mixing. The major challenges encountered in this field, demands the use of preprocessing techniques prior to hyperspectral image analysis. In this paper, we discuss the effective role of denoising by Legendre Fenchel Transformation (LFT) as a preprocessing method to improve the classification accuracy. Experimental time analysis shows that the computational efficiency of the proposed method is highly effective when compared with the existing preprocessing methods. LFT is based on the concept of duality which makes it a fast and reliable denoising strategy to effectively reduce the noise present in each band of the hyperspectral imagery, without losing much of the edge information. The denoising is performed on standard AVIRIS Indian Pines dataset. The performance of LFT denoising is evaluated by analysing the classification accuracy assessment measures. The denoised image is subjected to hyperspectral image classification using Multinomial Logistic Regression which learns the posterior probability distributions of each class. The potential of the proposed method is proved by the mean classification accuracy obtained experimentally without any post processing technique (94.4%), which is better when compared with the accuracies acquired by existing preprocessing techniques like Total Variation denoising and wavelet based denoising.

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2015

Conference Proceedings

K. Harikumar, Athira, S., Nair, Y. C., Sowmya V., and Soman, K. P., “ADMM based algorithm for spike and smooth signal separation using over-complete dictionary”, 4th IEEE International Conference on Communication and Signal Processing-ICCSP'15. IEEE, Adhiparasakthi Engineering College , Melmaruvathur , pp. 1617 - 1622, 2015.[Abstract]


In signal processing, many a time people deal with smooth stationary signals mixed with sharp spikes and most of the time their analysis demands separation of the smooth and spike elements. In this paper, we propose a methodology for this kind of separation based on the well-known notion of using an over complete dictionary to define an underdetermined system of linear equations and picking out its sparsest solution. The Alternating Direction Method of Multipliers (ADMM) framework is proposed for formulating and solving this optimization problem. The study of the performance of the algorithm with respect to certain signal parameters is also included. Performance of the algorithm was tested for different sparsity values at different signal energies and the results are reported. Cases involving group sparsity is also studied.

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2015

Conference Proceedings

N. Haridas, Sowmya V., and Soman, K. P., “Hyperspectral image classification using Random Kitchen Sink and Regularized Least Squares”, 4th IEEE International Conference on Communications and Signal Processing (ICCSP), 2015 . IEEE, Adhiparasakthi Engineering College , Melmaruvathur, pp. 1665 - 1669, 2015.[Abstract]


Kernel machines has gained considerable attention in the field of remote sensing for solving machine learning tasks, particularly in classification. Despite the fact that, kernel based methods produce comparatively better performance than traditional learning approaches, they are computationally expensive and requires large memory storage. In recent years, the concept of random features was introduced in kernel machines to solve this problem. This paper presents a new framework for hyperspectral image classification using Random Kitchen Sink (RKS) and Regularized Least Squares (RLS) classifier. The study shows that randomized features are economically powerful tool for hyperspectral image classification which produces significant improvement in classification accuracy. The proposed approach is tested on two standard hyperspectral datasets namely, Salinas-A and Indian Pines subset scene acquired by Airborne Visible Infrared Imaging Spectrometer (AVIRIS) sensor. A statistical comparison of the accuracies obtained on standard hyperspectral data with and without using Random Kitchen Sink algorithm for Regularized Least Squares classifier is analysed to show the effectiveness of the proposed method. The experimental results shows that the proposed method leads to improvement in Overall Accuracy from 85.12% to 98.58% and Kappa Coefficient from 0.8154 to 0.9822 for Salinas-A data scene. The analysis of Indian Pines subset scene shows that the proposed work results in significant improvement in Overall Accuracy from 62.76% to 93.79% and Kappa Coefficient from 0.5061 to 0.9160. The result analysis proves that random features of hyperspectral data as input to a standard linear classifier without the aid of any preprocessing produces better classification accuracy.

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2015

Conference Proceedings

P. G. Mol, Sowmya V., and Soman, K. P., “Performance enhancement of minimum volume-based hyperspectral unmixing algorithms by empirical wavelet transform”, International Conference on Soft Computing Systems, ICSCS 2015. AISC Springer Series, Noorul Islam Centre for Higher Education, Kumaracoil; India, 2015.[Abstract]


Hyperspectral unmixing of data has become one of the essential processing steps for crop classification. The endmembers to be extracted from the data are statistically dependent either in the linear or nonlinear form. The primary focus of this paper is on the effect of empirical wavelet transform (EWT) on hyperspectral unmixing algorithms based on the geometrical minimum volume approaches. The proposed method is experimented on the standard hyperspectral dataset, namely Cuprite. The performance analysis of proposed approach is eval- uated based on the standard quality metric called root mean square error (RMSE). The experimental result analysis shows that our proposed technique based on EWT improves the performance of hyperspectral unmixing algorithms based on the geometrical minimum volume approaches.

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2015

Conference Proceedings

S. Moushmi, Sowmya V., and Soman, K. P., “Empirical Wavelet Transform for Multifocus Image Fusion”, International Conference on Soft Computing Systems. AISC Springer Series, Noorul Islam Centre for Higher Education, Kumaracoil; India, p. 257--263, 2015.[Abstract]


Image fusion has enormous applications in the fields of satellite imaging, remote sensing, target tracking, medical imaging, and much more. This paper aims to demonstrate the application of empirical wavelet transform for the fusion of multifocus images incorporating the simple average fusion rule. The method proposed in this paper is experimented on benchmark datasets used for fusing images of different focuses. The effectiveness of the proposed method is evaluated across the existing techniques. The performance comparison of the proposed method is done by visual perception and assessment of standard quality metrics which includes root mean squared error, relative average spectral error, universal image quality index, and spatial information. The experimental result analysis shows that the proposed technique based on the empirical wavelet transform (EWT) outperforms the existing techniques.

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2015

Conference Proceedings

N. Nechikkat, Sowmya V., and Soman, K. P., “Variational Mode Feature-Based Hyperspectral Image Classification”, Second International Conference on Computer and Communication Technologies (IC3T)-2015. Springer, CMR Technical Campus, Hyderabad , pp. 365-373, 2015.[Abstract]


Hyperspectral image analysis is considered as a promising technology in the field of remote sensing over the past decade. There are various processing and analysis techniques developed that interpret and extract the maximum information from high-dimensional hyperspectral datasets. The processing techniques significantly improve the performance of standard algorithms. This paper uses variational mode decomposition (VMD) as the processing algorithm for hyperspectral data scenarios followed by classification based on sparse representation. Variational Mode Decomposition decomposes the experimental data set into few different modes of separate spectral bands, which are unknown. These modes are given as raw input to the classifier for performance analysis. Orthogonal matching pursuit (OMP), the sparsity-based algorithm is used for classification. The proposed work is experimented on the standard dataset, namely Indian pines collected by the airborne visible/infrared imaging spectrometer (AVIRIS). The classification accuracy obtained on the hyperspectral data before and after applying Variational Mode Decomposition was analyzed. The experimental result shows that the proposed work leads to an improvement in the overall accuracy from 84.82 to 89.78 %, average accuracy from 85.03 to 89.53 % while using 40 % data pixels for training.

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2015

Conference Proceedings

N. Haridas, Aswathy, C., Sowmya V., and Soman, K. P., “Hyperspectral Image Denoising Using Legendre-Fenchel Transform for Improved Sparsity Based Classification”, International Symposium on Intelligent Systems Technologies and Applications (ISTA-15). Springer, SCMS School of Engineering, Aluva, Kochi , pp. 521–528, 2015.[Abstract]


A significant challenge in hyperspectral remote sensing image analysis is the presence of noise, which has a negative impact on various data analysis methods such as image classification, target detection, unmixing etc. In order to address this issue, hyperspectral image denoising is used as a preprocessing step prior to classification. This paper presents an effective, fast and reliable method for denoising hyperspectral images followed by classification based on sparse representation of hyperspectral data. The use of Legendre-Fenchel transform for denoising is an effective spatial preprocessing step to improve the classification accuracy. The main advantage of Legendre-Fenchel transform is that it removes the noise in the image while preserving the sharp edges. The sparsity based algorithm namely, Orthogonal Matching Pursuit (OMP) is used for classification. The experiment is done on Indian Pines data set acquired by Airborne Visible Infrared Imaging Spectrometer (AVIRIS) sensor. It is inferred that the denoising of hyperspectral images before classification improves the Overall Accuracy of classification. The effect of preprocessing using Legendre Fenchel transformation is shown by comparing the classification results with Total Variation (TV) based denoising. A statistical comparison of the accuracies obtained on standard hyperspectral data before and after denoising is also analysed to show the effectiveness of the proposed method. The experimental result analysis shows that for 10%% training set the proposed method leads to the improvement in Overall Accuracy from 83.18%% to 91.06%%, Average Accuracy from 86.17%% to 92.78%% and Kappa coefficient from 0.8079 to 0.8981

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2015

Conference Proceedings

Sowmya V., Neethu Mohan, and Soman, K. P., “Edge Detection Using Sparse Banded Filter Matrices”, Second International Symposium on Computer Vision and the Internet (VisionNet’15). Elsevier Procedia Computer Science Journal, SCMS School of Engineering, Aluva, Kochi , pp. 10–17, 2015.[Abstract]


Edges are intensity change, which occur on the boundary between two regions in an image. Edges can be used as feature descriptors of an object. Hence, edge detection plays an important role in computer vision applications. This paper presents the application of sparse banded filter matrices in edge detection. The filter design is formulated in terms of banded matrices. The sparsity property of the designed filter leads to efficient computation. In our proposed method, we applied sparse banded high-pass filter row-wise and column-wise to extract the vertical and the horizontal edges of the image respectively. The proposed technique is experimented on standard images and the results are compared with the state-of-the-art methods. The visual comparison of the experimental results shows that the proposed approach for edge extraction based on sparse banded filter matrices produces result comparable to the existing methods. The advantage of the proposed approach is that the continuous edges are attained without any parameter tuning.

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2015

Conference Proceedings

N. Haridas, Sowmya V., and Soman, K. P., “Comparative Analysis of Scattering and Random Features in Hyperspectral Image Classification”, Second International Symposium on Computer Vision and the Internet (VisionNet’15). Elsevier Procedia Computer Science Journal, SCMS School of Engineering, Aluva, Kochi, pp. 307 – 314, 2015.[Abstract]


Hyperspectral images (HSI) contains extremely rich spectral and spatial information that offers great potential to discriminate between various land cover classes. The inherent high dimensionality and insufficient training samples in such images introduces Hughes phenomenon. In order to deal with this issue, several preprocessing techniques have been integrated in processing chain of HSI prior to classification. Supervised feature extraction is one such method which mitigates the curse of dimensionality induced by Hughes effect. In recent years, new strategies for feature extraction based on scattering transform and Random Kitchen Sink have been introduced, which can be used in context of hyperspectral image classification. This paper presents a comparative analysis of scattering and random features in hyperspectral image classification. The classification is performed using simple linear classifier such as Regularized Least Square (RLS) accessed through Grand Unified Regularized Least Squares (GURLS) library. The proposed approach is tested on two standard hyperspectral datasets namely, Salinas-A and Indian Pines subset scene captured by NASAs AVIRIS sensor (Airborne Visible Infrared Imaging Spectrometer). In order to show the effectiveness of proposed method, a comparative analysis is performed based on feature dimension, classification accuracy measures and computational time. From the comparative assessment, it is evident that classification using random features achieve excellent classification results with less computation time when compared with raw pixels(without feature extraction) and scattering features for both the datasets.

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2015

Conference Proceedings

C. Aswathy, Sowmya V., and K. P. Soman, “Hyperspectral Image Denoising using Low Pass Sparse Banded Filter Matrix for Improved Sparsity Based Classification”, Second International Symposium on Computer Vision and the Internet (VisionNet’15). Elsevier Procedia Computer Science Journal, SCMS School of Engineering, Aluva, Kochi, pp. 26–33, 2015.[Abstract]


The recent advance in sensor technology is a boon for hyperspectral remote sensing. Though Hyperspectral images (HSI) are captured using these advanced sensors, they are highly prone to issues like noise, high dimensionality of data and spectral mixing. Among these, noise is the major challenge that affects the quality of the captured image. In order to overcome this issue, hyperspectral images are subjected to spatial preprocessing (denoising) prior to image analysis (Classification). In this paper, authors discuss a sparsity based denoising strategy which uses low pass sparse banded filter matrices (AB filter) to effectively denoise each band of HSI. Both subjective and objective evaluations are conducted to prove the efficiency of the proposed method. Subjective evaluations involve visual interpretation while objective evaluations deals with the computation of quality matrices such as Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) index at different noise variance. In addition to these, the denoised image is followed by a sparsity based classification using Orthogonal Matching Pursuit (OMP) to evaluate the effect of various denoising techniques on classification. Classification indices obtained without and with applying preprocessing are compared to highlight the potential of the proposed method. The experiment is performed on standard Indian Pines Dataset. By using 10% of training set, a significant improvement in overall accuracy (84.21%) is obtained by the proposed method, compared to the other existing techniques.

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2015

Conference Proceedings

J. M Babu, Sowmya V., and Soman, K. P., “Fast Fourier Transform and Nonlinear Circuits Based Approach for Smart Meter Data Security”, International Conference onSmart Grid Technologies (ICSGT’15). Elsevier Procedia Technology, Amrita School of Engineering, Coimbatore, pp. 287–294, 2015.[Abstract]


Smart meters take measurements at minute intervals of time. The energy disaggregation of smart meter data provides accurate information regarding the electricity consumption but also reveals detailed information pertaining to the customer. Load signatures-that is unique for every appliance can be used to declare the behavioral pattern of the customers such as their sleep-wake cycles, usage period of various appliances, time during which the house is empty and by far, which particular channel is being viewed on the television. The smart meter data is transmitted to the utility company at regular intervals of time over the internet. Data that has such potential to access private information can be tampered with, by an undesirable third party during transmission. This is a vital privacy threat to the customer and has hyped the researches pertaining to smart meter data security in the recent times. To assure security, we introduce an algorithm that aims at encryption of data prior to transmission. The algorithm employs the FFT algorithm and various nonlinear systems to generate chaotic signals. The obtained chaotic signal is amalgamated with a transformed version of the smart meter data and securely transmitted over a suitable network. The proposed algorithm is tested on a real household's power signal data.

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2014

Conference Proceedings

Sowmya V., “Comparison of Performance Analysis of Image Classification by K-Means and Bisecting K-Means”, 4th National Conference on Advanced Trends in Information and Computing Sciences (NCATICS’14). KCT, Coimbatore, 2014.

2013

Conference Proceedings

M. Suchithra, Sukanya, P., Prabha, P., Sikha, O. K., Sowmya V., and Dr. Soman K. P., “An experimental study on application of orthogonal matching pursuit algorithm for image denoising”, 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013. IEEE, Kochi, Kerala, pp. 729-736, 2013.[Abstract]


<p>Signal or image reconstruction has now become a common task in many applications. According to linear algebra perspective, the number of measurements made or the number of samples taken for reconstruction must be greater than or equal to the dimension of signal or image. Also reconstruction follows the Shanon's sampling theorem which is based on the Nyquist sampling rate. The reconstruction of a signal or image using the principle of compressed sensing is an exception which makes use of only few number of samples which is below the sampling limit. Compressive sensing also known as sparse recovery aims to provide a better data acquisition and reduces computational complexities that occur while solving problems. The main goal of this paper is to provide clear and easy way to understand one of the compressed sensing greedy algorithm called Orthogonal Matching Pursuit (OMP). The OMP algorithm involves the concept of overcomplete dictionary that is formulated based on different thresholding methods. The proposed method gives the simplified approach for image denoising by using OMP only. The experiment is performed on few standard image data set simulated with different types of noises such as Gaussian noise, salt and pepper noise, exponential noise and Poisson noise. The performance of the proposed method is evaluated based on the image quality metric, Peak Signal-to-Noise Ratio (PSNR). © 2013 IEEE.</p>

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2013

Conference Proceedings

G. Aarthy, Amitha, P. L., Krishnan, T., Pillai, G. S., Sowmya V., and Dr. Soman K. P., “A comparative study of spike and smooth separation from a signal using different overcomplete dictionary”, 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013. IEEE, Kochi, Kerala, pp. 590-595, 2013.[Abstract]


Most of the natural signals are complex and are highly time varying, since they are non stationary in nature. In this paper, a comparative study for separating spikes and smooth signal components from a non-stationary signal are performed based on different overcomplete dictionaries. The experiment is evaluated using the sparse representation with different bases such as the Discrete Cosine Transform (DCT), Walsh-Hadamard, Orthogonal and Biorthogonal wavelet basis. The primary focus of this paper is to use L1 minimization for retrieving the smooth and spikes component of the signal using different overcomplete dictionary. The experimental results reveals out the dictionary that delivers a better separation without distorting temporal and spectral characteristics. © 2013 IEEE.

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2013

Conference Proceedings

B. G. Gowri, Hariharan, V., Thara, S., Sowmya V., Kumar, S. S., and Dr. Soman K. P., “2D Image data approximation using Savitzky Golay filter - Smoothing and differencing”, 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013. IEEE, Kochi, Kerala, pp. 365-371, 2013.[Abstract]


Smoothing and differencing is one of the major important and necessary step in the field of signal processing, image processing and also in the field on analytical chemistry. The search for an efficient image smoothing and edge detection method is a challenging task in image processing sector. Savitzky Golay Filters are one among the widely used filters for analytical chemistry. Even though they have exceptional features, they are rarely used in the field of image processing. The designed filter is applied for image smoothing and a mathematical model based on partial derivatives is proposed to extract the edges in images. The smoothing technique of SG filter offers an extremely simple aid in extracting the edge information. An approach using SG filter which can be applied in preserving edge information is one of the major tasks involved in the classification process in the domain of Optical Character Recognition. The paper is focused on designing the Savitzky Golay filter by using the concepts of linear algebra. The main objective of the paper is to portray a clear cut idea about Savitzky Golay filter and to study the design of Savitsky Golay filters based on the concepts of Linear Algebra. © 2013 IEEE.

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2012

Conference Proceedings

P. K. Indukala, Lakshmi, K., Sowmya V., and Dr. Soman K. P., “Implementation of ℓ 1 magic and one bit compressed sensing based on linear programming using excel”, International Conference on Advances in Computing and Communications, ICACC 2012. IEEE, Kochi, Kerala, pp. 69-72, 2012.[Abstract]


<p>Compressed sensing helps in the reconstruction of sparse or compressible signals from small number of measurements. The sparse representation has great importance in modern signal processing. The main objective is to provide a strong understanding of the concept behind the theory of compressed sensing by using the key ideas from linear algebra. In this paper, the concept of compressed sensing is explained through an experiment formulated based on linear programming and solved using l1 magic and One bit compressed sensing methods in Excel. © 2012 IEEE.</p>

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2012

Conference Proceedings

R. Anand, Prabha, P., Sikha, O. K., Suchithra, M., Dr. Soman K. P., and Sowmya V., “Visualization of OFDM using Microsoft Excel spreadsheet in Linear Algebra Perspective”, International Conference on Advances in Computing and Communications, ICACC 2012. IEEE, Kochi, Kerala, pp. 58-64, 2012.[Abstract]


Orthogonal Frequency Division Multiplexing (OFDM) is one of the leading technology that is ruling the communication field. But unfortunately, it is shrouded in mystery. A good knowledge in Linear Algebra is required to appreciate the technology in a better way. So the work focuses on explaining OFDM system from linear algebra point of view. Also, OFDM model communication system is simulated using Excel which makes ease for anyone experiment with OFDM and understand the underlying principle. The paper aims to provide strong foundation on the concept behind OFDM without the need of having much knowledge in electronics field. © 2012 IEEE.

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2012

Conference Proceedings

P. Prabha, Sikha, O. K., Suchithra, M., Sukanya, P., Sowmya V., and Dr. Soman K. P., “Computation of continuous wavelet transform using microsoft excel spreadsheet”, Proceedings - 2012 International Conference on Advances in Computing and Communications, ICACC 2012. IEEE, Kochi, Kerala, pp. 73-77, 2012.[Abstract]


Wavelet theory has become an essential and significant tool for signal and image processing applied in the analysis of various real time signals. It is thus necessary to include wavelet transform and its application in multifractal analysis as a part of the engineering curriculum. In this paper, we present simple and effective way of computing Continuous Wavelet Transform (CWT) using Microsoft Excel Spreadsheet which serves as an user friendly mathematical tool for beginners. The motivation of this paper is to prove the computational power of excel, using which students can have better understanding of the basic concept behind the computation of Continuous Wavelet Transform. The plot of Continuous Wavelet Transform of Brownian signal computation in Excel is compared with that of the result in the Matlab Toolbox. The singularities present in the signal can be inferred from the wavelet modulus maxima plot. The visual interpretation proves that Excel tool provides computational power comparable to that of the Matlab software. The codes for the implementation of CWT in Excel are available on nlp.amrita.edu:8080/sisp/wavelet/cwt/cwt.xlsm, nlp.amrita.edu:8080/sisp/wavelet/cwt/modmax.xlsm, nlp.amrita.edu:8080/sisp/ wavelet/cwt/thermo.xlsm © 2012 IEEE.

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2011

Conference Proceedings

B. D Bhushan, Sowmya V., M Manikandan, S., and Soman, K. P., “An effective pre-processing algorithm for detecting noisy spectral bands in hyperspectral imagery”, International Symposium on Ocean Electronics, SYMPOL 2011. IEEE, Kochi, pp. 34 - 39, 2011.[Abstract]


In this paper, we present an effective pre-processing algorithm for band selection approach which is an essential task in hyperspectral image analysis. The pre-processing algorithm is developed based on the average inter-band block-wise correlation coefficient measure and a simple thresholding strategy. Here, the threshold parameter is found based on the standard deviation of the average inter-band block-wise correlation coefficients. The performance of the proposed algorithm is validated using the standard hyperspectral database created by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. By comparing the detected bands with ground-truth annotations, we observed that the proposed algorithm identifies the noisy and water absorption bands in the high-dimensional hyperspectral images. The proposed algorithm achieves the classification accuracy of 94.73%.

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2011

Conference Proceedings

Sowmya V., “A Decision Tree Based Land Cover Image Classification Using Color Space and Texture”, IEEE International Conference on Computational Intelligence and Computing Research. IEEE, Cape Institute of Technology, Levengipuram, Kanyakumari, India, 2011.

2010

Conference Proceedings

B. D. Bhushan, Sowmya V., and Dr. Soman K. P., “Super resolution blind reconstruction of low resolution images using framelets based fusion”, International Conference on Recent Trends in Information, Telecommunication, and Computing ITC 2010. IEEE, Kochi, Kerala, pp. 100-104, 2010.[Abstract]


In this paper, we propose a fusion technique based on framelets to obtain super resolution image from sub-pixel shifted, noisy, blurred low resolution images. This method has high advantages over all existing methods. A Tight frame filter bank provides symmetry and has a redundancy that allows for approximate shift invariance which leads to clear edges, high spatial information with effective denoising which was lacked in critically sampled discrete wavelet transform. They are also shorter and results in smoother scaling and wavelet functions. The reconstructed super resolution image obtained by this technique has high peak signal to noise ratio (PSNR) and low mean square error (MSE) than that obtained by wavelet based fusion method, which is evident through the experimental results. © 2010 IEEE.

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Publication Type: Conference Paper

Year of Conference Publication Type Title

2017

Conference Paper

Sowmya V., Dr. Govind D., and Dr. Soman K. P., “Significance of contrast and structure features for an improved color image classification system”, in 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), 2017, pp. 12-14 .[Abstract]


In general, the three main modules of color image classification systems are: color-to-grayscale image conversion, feature extraction and classification. The color-to-grayscale image conversion is the important pre-processing step which must incorporate the significant and discriminative contrast and structure information in the converted grayscale images as in the original color image. All the existing techniques for color-to-grayscale image conversion preserves the significant contrast and structure information in the converted grayscale images in different manners. Hence, the present work is to analyze the significant and discriminative contrast and structure information preserved in the converted grayscale images using two different decolorization techniques called rgb2gray and singular value decomposition based color-to-grayscale image conversion (SVD) applied in the color image classification systems using the three different proposed features. The three different features for color image classification systems are proposed based on the combination of the existing dense SIFT features and the contrast & structure content computed using color-to-gray structure similarity index (C2G-SSIM) metric. More »»

2017

Conference Paper

Sowmya V., Ajay, A., Dr. Govind D., and Dr. Soman K. P., “Improved color scene classification system using deep belief networks and support vector machines”, in 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), 2017, pp. 12-14 .[Abstract]


In general, the three main modules of the color scene classification systems are image decolorization, feature extraction and classification. The work presented in this paper focuses on image decolorization and classification as two stages. The first stage or objective of this paper is to improve the performance of the color scene classification system using deep belief networks (DBN) and support vector machines (SVM). Therefore, color scene classification system termed as AGMM-DBN-SVM is proposed using the existing feature extraction technique called bags of visual words (BoW) derived from the dense scale-invariant feature transform (SIFT) and adapted gaussian mixture models (AGMM). The second stage of the presented work is to combine the proposed AGMM-DBN-SVM classification models obtained for the two different image decolorization methods called rgb2gray and singular value decomposition (SVD) based color-to-grayscale image mapping techniques to significantly increase the performance of the proposed color scene classification system. The effectiveness of the proposed framework is experimented on Oliva Torralba (OT) scene dataset containing 8 different classes. The classification rate of the proposed color scene classification system applied on OT 8 scene dataset is significantly greater than the one of the existing benchmarks color scene classification system developed using AGMM and SVM. More »»

2016

Conference Paper

P. A, R, M., Sowmya V., and Dr. Soman K. P., “X-ray Image Classification Based On Tumor using GURLS and LIBSVM”, in International Conference on Communications and Signal Processing (ICCSP’16), Adhiparasakthi Engineering College, Melmaruvathur , 2016.[Abstract]


In today's world, X-ray imaging is the low cost diagnostic technique when compared with all other medical imaging techniques. In this paper, the proposed method is to classify X-ray images based on tumor. The features are extracted using Singular Value Decomposition (SVD) and classified using different kernels in Library for Support Vector Machine (Lib-SVM) and Grand Unified Regularized Least Squares (GURLS). The proposed method is experimented on X-ray image dataset which is approved by an Oncologist. The effectiveness of proposed method is validated based on classification parameters. The experiment result analysis shows that Gaussian-ho in GURLS provides 95% classification accuracy which is 5% higher than RBF kernel in LibSVM. The performance of the proposed system is validated by an Oncologist.

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2016

Conference Paper

M. P, M, S., Sowmya V., and Dr. Soman K. P., “Low Contrast Satellite Image Restoration based on adaptive Histogram Equalization and Discrete Wavelet Transform”, in - 5th IEEE International Conference on Communication and Signal Processing-ICCSP'15, Adhiparasakthi Engineering College, Melmaruvathur , 2016.[Abstract]


Normally images obtained from satellites are of low-contrast type which hides major information carried by the image. Hence, image restoration is necessary in the image processing domain to extract all the information present in the images. The low contrast satellite image restoration based on adaptive histogram equalization combined with Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) is proposed in this paper. The proposed technique is experimented on three different satellite images. The effectiveness of the method introduced in this paper is shown by comparing it against the existing techniques based on gamma correction and histogram equalization combined with DCT and DWT. The comparison is done based on the standard parameters called Peak Signal to Noise Ratio (PSNR) and Standard Deviation. The result and analysis on the basis of PSNR values shows that adaptive histogram equalization combined with DWT is more effective approach compared to adaptive histogram equalization combined with DCT.

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2016

Conference Paper

R. Reshma, Sowmya V., and Soman, K. P., “Dimensionality Reduction Using Band Selection Technique for Kernel Based Hyperspectral Image Classification”, in 6th International Conference on Advances in Computing and Communications , ICACC-2016, Rajagiri School of Engineering & Technology, 2016, vol. 93, pp. 396-402.[Abstract]


Hyperspectral images have abundant of information stored in the various spectral bands ranging from visible to infrared region in the electromagnetic spectrum. High data volume of these images have to be reduced, preserving the original information, to ensure efficient processing. In this paper, dimensionality reduction is done on Indian Pines and Salinas-A datasets using inter band block correlation coefficient technique followed by Singular Value Decomposition (SVD) and QR decomposition. The dimensionally reduced images are classified using GURLS and LibSVM. Classification accuracies of the original image is compared to that of the dimensionally reduced image. The experimental analysis shows that, for 10% training sample the overall accuracy, average accuracy and kappa coefficient of the dimensionally reduced image (about 50% of the dimension is reduced) is i)83.52%, 77.18%, 0.8110 for Indian Pines and ii)99.53%, 99.40%, 0.9941 for Salinas-A dataset which is comparable to that of original image i)84.67%, 82.28%, 0.8247 for Indian Pines and ii)99.32%, 99.18%, 0.9916 for Salinas-A dataset. © 2016 The Authors. Published by Elsevier B.V.

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2016

Conference Paper

S. Srivatsa, Ajay, A., Chandni, C. K., Sowmya V., and Soman, K. P., “Application of Least Square Denoising to Improve ADMM Based Hyperspectral Image Classification”, in 6th International Conference on Advances in Computing and Communications , ICACC-2016, Rajagiri School of Engineering & Technology, 6-8 September 2016, 2016, vol. 93, pp. 416-423.[Abstract]


Hyperspectral images contain a huge amount of spatial and spectral information so that, almost any type of Earth feature can be discriminated from any other feature. But, for this classification to be possible, it is to be ensured that there is as less noise as possible in the captured data. Unfortunately, noise is unavoidable in nature and most hyperspectral images need denoising before they can be processed for classification work. In this paper, we are presenting a new approach for denoising hyperspectral images based on Least Square Regularization. Then, the hyperspectral data is classified using Basis Pursuit classifier, a constrained L1 minimization problem. To improve the time requirement for classification, Alternating Direction Method of Multipliers (ADMM) solver is used instead of CVX (convex optimization) solver. The method proposed is compared with other existing denoising methods such as Legendre-Fenchel (LF), Wavelet thresholding and Total Variation (TV). It is observed that the proposed Least Square (LS) denoising method improves classification accuracy much better than other existing denoising techniques. Even with fewer training sets, the proposed denoising technique yields better classification accuracy, thus proving least square denoising to be a powerful denoising technique. © 2016 The Authors. Published by Elsevier B.V.

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2015

Conference Paper

L. S. Kiran, Sowmya V., and Soman, K. P., “Dimensionality reduced recursive filter features for hyperspectral classification”, in Second International Conference on Computer and Communication Technologies (IC3T)-2015, CMR Technical Campus, Hyderabad, 2015, vol. 380, pp. 557-565.[Abstract]


Dimensionality reduction techniques have been immensely used in hyperspectral image classification tasks and is still a topic of great interest. Feature extraction based on image fusion and recursive filtering (IFRF) is a recent work which provides a framework for classification and produces good classification accuracy. In this paper, we propose an alternative approach to this technique by employing an efficient preprocessing technique based on average interband blockwise correlation coefficient followed by a stage of dimensionality reduction. The final stages involve recursive filtering and support vector machine (SVM) classifier. Our method highlights the utilization of an automated procedure for the removal of noisy and water absorption bands. Results obtained using experimentation of the proposed method on Aviris Indian Pines database indicate that a very low number of feature dimensions provide overall accuracy around 98%. Four different dimensionality reduction techniques (LDA, PCA, SVD, wavelet) have been employed and notable results have been obtained, especially in the case of SVD (OA = 98.81) and wavelet-based approaches (OA = 98.87). © Springer India 2016.

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2015

Conference Paper

N. John, Viswanath, A., Sowmya V., and Soman, K. P., “Analysis of various color space models on effective single image super resolution”, in International Symposium on Intelligent Systems Technologies and Applications (ISTA-15), , ICACCI 2015; Kochi; India, 2015, vol. 384, pp. 529-540.[Abstract]


Color models are used for facilitating the specification of colors in a standard way. A suitable color model is associated with every application based on color space. This paper mainly focuses on the analysis of effectiveness of different color models on single image scale-up problems. Single image scale-up aims in the recovery of original image, where the input image is a blurred and down- scaled version of the original one. In order to identify the effect of different color models on scale-up of single image applications, the experiment is performed with the single image scale-up algorithm on standard image database. The performance of different color models (YCbCr, YCoCg, HSV, YUV, CIE XYZ, Photo YCC, CMYK, YIQ, CIE Lab, YPbPr) are measured by quality metric called Peak Signal to Noise Ratio (PSNR). The experimental results based on the calculated PSNR values prove that YCbCr and CMYK color models give effective results in single image scale-up application when compared with the other available color models. © Springer International Publishing Switzerland 2016.

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2015

Conference Paper

A. Joy, Merlin, D., .K, D., Sowmya V., and , “Aerial Image Classification using GURLS and LIBSVM”, in 5th IEEE International Conference on Communication and Signal Processing-ICCSP'15, Adhiparasakthi Engineering College, Melmaruvathur , 2015.[Abstract]


Image classification using kernels have very great importance in remote sensing data. The goal of this work is to efficiently classify the large set of aerial images into different classes. This paper introduces a kernel based classification for aerial images. It uses Grand Unified Regularized Least Square (GURLS) and library for support vector machines (LIBSVM). This paper compares the performance of different kernel methods used in GURLS and LIBSVM. The experiment is performed on three sets of aerial image data sets which are obtained from electrical engineering department of Banja Luka University under the DSP laboratory, funded by the WUSAUSTRIA project of the European Union. From the experiment performed, it can be deduced that GURLS library is better compared to LIBSVM in terms of its prediction accuracy. The advantage of GURLS library package over LIBSVM is its automatic parameter selection.

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2014

Conference Paper

Sowmya V., “Image Classification using Deep Learning Features”, in International Conference on Emerging Trends in Electrical Engineering-ICETREE , Thangal Kunju Musaliar College of Engineering , 2014.

2014

Conference Paper

S. Santhosh, Abinaya, N., Rashmi, G., Sowmya V., and Dr. Soman K. P., “A novel approach for denoising coloured remote sensing image using Legendre Fenchel Transformation”, in 2014 International Conference on Recent Trends in Information Technology, ICRTIT 2014, https://www.scopus.com/record/display.uri?eid=2-s2.0-84921058967&origin=inward&txGid=0, 2014.[Abstract]


Data acquired from remote sensing satellites are processed in order to retrieve the information from an image. Those images are preprocessed using image processing techniques such as noise removal. Satellite images are assumed to be corrupted with white Gaussian noise of zero mean and constant variance. Three planes of the noisy image are denoised separately through Legendre Fenchel Transformation. Later, these three planes are concatenated and compared with results obtained by Euler-Lagrange ROF model. Simulation results show that Legendre Fenchel ROF is highly convergent and less time consuming. To add evidence to the outcomes, quality metrics such as variance and PSNR for noisy and denoised images are calculated. The qualitative analysis of an image is analysed using MSSIM calculations, which clarifies the Structural Similarity between denoised images with original image. © 2014 IEEE.

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Publication Type: Newspaper Article

Year of Conference Publication Type Title

2013

Newspaper Article

Sowmya V., “Voice Your Views -2 – Is Coimbatore Safe for Women?”, The Hindu Metroplus, CBE Edition, Coimbatore, 2013.

2013

Newspaper Article

Sowmya V., “Voice Your Views -3- Favorite yet unusual getaways at Coimbatore”, The Hindu Metroplus, CBE Edition, Coimbatore, 2013.

2013

Newspaper Article

Sowmya V., “Voice Your Views -4- What do you think should be censored from movies?”, The Hindu Metroplus, CBE Edition, Coimbatore, 2013.

2013

Newspaper Article

Sowmya V., “Voice Your Views -5 –Should the death penalty be abolished?”, The Hindu Metroplus, CBE Edition , Coimbatore, 2013.

2012

Newspaper Article

Sowmya V., “Voice Your Views -1 –Is there a way to segregate the volume of garbage we generate?”, The Hindu Metroplus, CBE Edition, Coimbatore, 2012.

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