Qualification: 
M.Tech
Email: 
v_sowmya@cb.amrita.edu

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).
  • Registered for Ph. D. on July 2013 at Amrita Vishwa Vidyapeetham, Coimbatore.
  • Promoted as Assistant Professor (Senior Grade) on Nov, 2015.

RESEARCH AREA

  • Color- to- grayscale image conversion
  • Image Quality Metric

PROFESSIONAL ACTIVITIES

  • 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.
  • Event Coordinator - ‘Cook without Fire or Wire’ of Amrita Cultural Fest “Amritotsavam-2015’.
  • 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 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 “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.
  • 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 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 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.

PRESENTATIONS

  • 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 2011 IEEE International Conference on Computational Intelligence and Computing Research, at Cape Institute of Technology, Levengipuram, Kanyakumari, India.
  • 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.
  • Rendered Invited Guest Lecture on “Remote Sensing and Applications of GIS” at Avinashilingam University, Coimbatore.
  • 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 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.
  • 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.
  • 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)

INVITED TALKS

  • Delivered a lecture on “Data Mining” for MBA students of Avinashilingam University on March 18, 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 December 5, 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 December 18, 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 December 4 - 5, 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, Coimbatore, during January 30 – February 2, 2014.

AWARDS

  • 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 cash award of Rs.5000 for securing First Place in the paper presented in National Level Seminar on Signal Processing held at SreeNarayanaGuru Institute of Science and technology, Kochi.
  • 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 PG Merit Scholarship for University Rank Holders - Rs.40000 by University Grants Commission (UGC), New-Delhi in the year 2008.
  • Awarded with “Swami Vivekananda Award” for Excellence in Education by Yuva Kendra Association, Madurai.
  • 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” that is celebrated on 5 March 2016 at Radha Regent Chennai.

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.

Publications

Publication Type: Journal Article

Year of Conference Publication Type Title

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. 1–8, 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”, Journal of Intelligent and Fuzzy Systems, vol. 32, 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.

2016

Journal Article

S. Moushmi, Sowmya V., and Dr. Soman K. P., “Empirical wavelet transform for multifocus image fusion”, Advances in Intelligent Systems and Computing, vol. 397, pp. 257-263, 2016.[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 multi- focus 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. © Springer India 2016. More »»

2016

Journal Article

P. G. Mol, Sowmya V., and Soman, K. P., “Performance enhancement of minimum volume-based hyperspectral unmixing algorithms by empirical wavelet transform”, Advances in Intelligent Systems and Computing, vol. 397, pp. 251-256, 2016.[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. © Springer India 2016. 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

N. Nechikkat, Sowmya V., and Soman, K. P., “Variational mode feature-based hyperspectral image classification”, Advances in Intelligent Systems and Computing, vol. 380, pp. 365-373, 2016.[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. © Springer India 2016. 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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2015

Journal Article

Y. C. Nair, Binsha, P., V. Pradeep, V., Sowmya V., and Soman, K. P., “Spreadsheet implementation of random kitchen sink for classification”, International Journal of Applied Engineering Research, vol. 10, no. 20, pp. 19228-19233, 2015.[Abstract]


In order to excavate on large chunks of unstructured data to retrieve the nuggets of knowledge, a lot of technically sound algorithms have been developed. In this paper, the classification of data has been performed in an efficient manner by inheriting concepts from linear algebra and optimization theory. This paper demonstrates the implementation of the mathematical ideas behind mapping input data to higher dimension using Random Kitchen Sink (RKS) and implementation of classification algorithm using Regularized Least Square (RLS) estimation. In this context, computational thinking methodology is accomplished using the most versatile tool available for non-programmers, that is excel. This paper elucidates a number of excel utilities. © Research India Publications. More »»

2015

Journal Article

S. Athira, Harikumar, K., Sowmya V., and Soman, K. P., “Parameter analysis of random kitchen sink algorithm”, International Journal of Applied Engineering Research, vol. 10, no. 20, pp. 19351-19355, 2015.[Abstract]


Classification is one of the main stream areas in machine learning. Many a time we have to deal with data sets that are not classifiable using a linear separator. This paper presents a theoretical and an experimental study of random kitchen sink algorithm which makes it possible for such data sets to become linearly separable by efficiently, effectively and explicitly mapping it to an appropriate higher dimensional space. It explores the dependency of various parameters of the algorithm, mainly the dimension and variance of the concerned random variable on the accuracy of classification, and how to fine tune these parameters to obtain the best of results. It also presents an intuitive understanding of how these parameters actually affect the accuracy of classification by connecting it with the shift invariant RBF kernel. The effect of choice of these parameters on a two class classification problem is also included. © Research India Publicatio 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

Sowmya V., Neethu Mohan, and Soman, K. P., “Sparse banded matrix filter for image denoising”, Indian Journal of Science and Technology, vol. 8, no. 24, 2015.[Abstract]


Noise is one of the prime factors which degrade the quality of an image. Hence, image denoising is an essential image enhancement technique in the image processing domain. In this paper, we use low-pass sparse banded filter matrices for image denoising. Sparsity is the key concept in this filter design. We applied the designed low-pass filter both row-wise and column-wise to denoise the image. The proposed method is experimented on standard test images corrupted with different types of noises namely Gaussian, White Gaussian, Salt & Pepper and Speckle with noise level equals to 0.01, 0.05 and 0.1. The effectiveness of the proposed method of denoising is evaluated by the computation of standard quality metric known as Peak Signal-to-Noise Ratio (PSNR). The experimental result analysis shows that the proposed image denoising technique based on sparse banded filter matrices results in significant improvement in PSNR around 2dB to 8dB for different type of noises with noise level equal to 0.1 and is also aided by the visual analysis. More »»

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

C. Aswathy, Sowmya V., and Soman, K. P., “ADMM based hyperspectral image classification improved by denoising using legendre fenchel transformation”, Indian Journal of Science and Technology, vol. 8, no. 24, 2015.[Abstract]


This paper discusses about a sparsity based algorithm used for Hyperspectral Image (HSI) classification where the test pixel vectors are sparsely represented as the linear combination of a few number of training samples from a well-organised dictionary matrix. The sparse vector is obtained using Basis Pursuit (BP) which is a constrained l4 minimization problem. This problem is solved by using a simple and powerful iterative algorithm known as Alternating Direction Method of Multipliers (ADMM) which significantly reduces the computational complexity of the problem and thereby speeds up the convergence. The classification accuracy is considerably improved by including efficient preprocessing techniques to remove the unwanted information (noise) present in Hyperspectral images. This paper uses a fast and reliable denoising technique based on Legendre Fenchel Transformation (LFT) to effectively denoise each band of HSI prior to ADMM based classification (proposed method). A comparison of proposed technique with one of the convex optimization tools namely, CVX is given to exhibit the fast convergence of the former method. The experiment is performed on standard Indian Pines dataset captured using AVIRIS sensor. The potential of the proposed method is illustrated by analyzing the classification indices obtained with and without applying any preprocessing methods. With only 10% training set, an overall accuracy of 96.76% is obtained for the proposed method at a much faster rate compared to computation time taken by CVX solver. More »»

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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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.

2017

Conference Proceedings

Sowmya V., D .Govind, and K. P. Soman, “Significance of Contrast and Structure Features for an Improved Color Image Classification System”, 2017 International Conference on Signal and Image Processing, ICSIPA-2017. IEEE-Scopus, ISI Web of Science, Kuching, Malaysia, pp. 12-14 , 2017.

2017

Conference Proceedings

Sowmya V., Aleena Ajay, .Govind, D., and K. P. Soman, “Improved Color Scene Classification System using Deep Belief Networks and Support vector Machines”, 2017 International Conference on Signal and Image Processing, ICSIPA-2017. IEEE-Scopus, ISI Web of Science, Kuching, Malaysia, pp. 12-14 , 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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Publication Type: Conference Paper

Year of Conference Publication Type Title

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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M. Tech. Thesis

Year M.Tech Thesis 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
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