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 (University), 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 (University), 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 (University), 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 (University), 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 (University), 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 (University), 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.
  • 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.
  • 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)
  • 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 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.
     

AWARDS

  • 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 to be 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
2016 Journal Article L. S. Kiran, Sowmya, V., and 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 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 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 H. T. Suseelan, Sudhakaran, S., Sowmya, V., and Soman, K. P., “Performance Evaluation of Sparse Banded Filter Matrices using content based image retrieval”, Institute of Integrative Omics and Applied Biotechnology, vol. 7, 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. More »»
2016 Journal Article A. M, N, D., Sowmya, V., Mahan, N., and 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 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 V. Sowmya, Govind, D., and Soman, K. P., “Significance of incorporating chrominance information for effective color-to-grayscale image conversion”, Signal, Image and Video Processing, pp. 1–8, 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 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, 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 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, 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 V. Sowmya, Aswathy C., Haridas, N., and SOMAN, K. P., “Effect of AB filter denoising on ADMM based Hyperspectral Image Classification”, Department of Computer Science, Rajagiri School of Social Sciences, Kalamassery, Kochi , pp. 127-131, 2015.
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 Conference on Knowledge Engineering – Theory and Practices (KETP-15), pp. 95-98, 2015.
2015 Journal Article V. Sowmya, S., M., and Soman, K. P., “Performance Comparison of Empirical wavelet Transform and Empirical Mode Decomposition on Pan sharpening”, International Conference on Knowledge Engineering – Theory and Practices (KETP-15), pp. 107-111, 2015.
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, 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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Publication Type: Conference Paper
Year of Conference Publication Type Title
2016 Conference Paper R. R, Sowmya, V., and Soman, K. P., “Improvement in kernel based Hyperspectral image classification using legendre fenchel denoising”, in International Conference on Innovation in Information Embedded and Communication Systems (ICIIECS’16), , 2016.
2016 Conference Paper V. Pradeep V, Sowmya, V., and Soman, K. P., “Variational Mode Decomposition based Multispectral and Panchromatic Image Fusion”, in International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems (ICAIECES-2016), 2016.
2016 Conference Paper A. Joy, Merlin, D., .K, D., Sowmya, V., and , “Aerial Image Classification using GURLS and LIBSVM”, in International Conference on Innovation in Information Embedded and Communication Systems (ICIIECS’16), 2016.
2016 Conference Paper V. P.V, G, R. Devi, Sowmya, V., and Soman, K. P., “Least Square based image denoising using wavelet filters”, in International Conference on Innovation in Information Embedded and Communication Systems (ICIIECS’16), 2016.
2016 Conference Paper S. Rajan, Sowmya, V., and Soman, K. P., “Low Contrast Image Enchancement using Adaptive Histogram, Discrete Wavelet and Cosine Transform”, in International Conference on Innovation in Information Embedded and Communication Systems (ICIIECS’16), 2016.
2016 Conference Paper A. B, se, S., Sowmya, V., and Soman, K. P., “Performanve Evaluation of Edge Feature Extracted using Sparse Banded Matrix Filter Based on Face Recognition”, in International conference on Soft computing systems (ICSCS’16), 2016.
2016 Conference Paper V. Pradeep V, R, R., Sowmya, V., and Soman, K. P., “Comparative Analysis of sparsity based and kernel based algorithms for Hyperspectral Image Classification”, in International conference on Soft computing systems (ICSCS’16), 2016.
2016 Conference Paper P. A, R, M., Sowmya, V., and Soman, K. P., “X-ray Image Classification Based On Tumor using GURLS and LIBSVM”, in International Conference on Communications and Signal Processing (ICCSP’16), 2016.
2016 Conference Paper S. M, K.S, G. Krishnan, Sowmya, V., and Soman, K. P., “Image denoising based on weighted Regularized Least Squares”, in International conference on Soft computing systems (ICSCS’16), 2016.
2016 Conference Paper M. P, M, S., Sowmya, V., and Soman, K. P., “Low Contrast Satellite Image Restoration based on adaptive Histogram Equalization and Discrete Wavelet Transform”, in International Conference on Communications and Signal Processing (ICCSP’16), 2016.
2016 Conference Paper V. Sowmya, “Least Square based Signal Denoising and Deconvolution using Wavelet Filters”, in Third International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS-15),, Karpagam College of Engineering, 2016.
2014 Conference Paper V. Sowmya, “Image Classification using Deep Learning Features”, in International Conference on Emerging Trends in Electrical Engineering-ICETREE , Thangal Kunju Musaliar College of Engineering , 2014.
2013 Conference Paper B. G. Gowri, Hariharan, V., Thara, S., Sowmya, V., Kumar, S. S., and SOMAN, K. P., “2D Image data approximation using Savitzky Golay filter #x2014; Smoothing and differencing”, in Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), 2013 International Multi-Conference on, 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.

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Publication Type: Conference Proceedings
Year of Conference Publication Type Title
2016 Conference Proceedings V. Sowmya, “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 V. Sowmya, “l1 Trend Filter for Image Denoising”, 6th International Conference on Advances in Computing and Communications , ICACC-2016. Rajagiri School of Engineering & Technology, 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 V. Sowmya, Mohan, N., 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 SOMAN, K. P., “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 V. Sowmya, “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 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 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 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 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., 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 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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Publication Type: Newspaper Article
Year of Conference Publication Type Title
2013 Newspaper Article V. Sowmya, “Voice Your Views -2 – Is Coimbatore Safe for Women?”, The Hindu Metroplus, CBE Edition, Coimbatore, 2013.
2013 Newspaper Article V. Sowmya, “Voice Your Views -3- Favorite yet unusual getaways at Coimbatore”, The Hindu Metroplus, CBE Edition, Coimbatore, 2013.
2013 Newspaper Article V. Sowmya, “Voice Your Views -4- What do you think should be censored from movies?”, The Hindu Metroplus, CBE Edition, Coimbatore, 2013.
2013 Newspaper Article V. Sowmya, “Voice Your Views -5 –Should the death penalty be abolished?”, The Hindu Metroplus, CBE Edition , Coimbatore, 2013.

Pages

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
Faculty Details

Qualification:

Designation: 
Faculty Email: 
v_sowmya@cb.amrita.edu