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: Conference Paper

Year of Conference Publication Type Title

2015

Conference Paper

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


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

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2015

Conference Paper

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


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

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2015

Conference Paper

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


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

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2014

Conference Paper

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

2014

Conference Paper

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


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

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Publication Type: Conference Proceedings

Year of Conference Publication Type Title

2015

Conference Proceedings

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


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

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2015

Conference Proceedings

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


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

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2015

Conference Proceedings

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


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

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2015

Conference Proceedings

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


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

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2015

Conference Proceedings

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


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

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2015

Conference Proceedings

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


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

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2014

Conference Proceedings

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

2013

Conference Proceedings

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


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

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2013

Conference Proceedings

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


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

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2013

Conference Proceedings

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


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

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2012

Conference Proceedings

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


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

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2012

Conference Proceedings

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


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

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2012

Conference Proceedings

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


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

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2011

Conference Proceedings

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


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

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2011

Conference Proceedings

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

2010

Conference Proceedings

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


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

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Publication Type: Journal Article

Year of Conference Publication Type Title

2015

Journal Article

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


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

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2015

Journal Article

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


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

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2015

Journal Article

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


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

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2014

Journal Article

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


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

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2014

Journal Article

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


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

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2012

Journal Article

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


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

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2012

Journal Article

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


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

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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
207
PROGRAMS
OFFERED
5
AMRITA
CAMPUSES
15
CONSTITUENT
SCHOOLS
A
GRADE BY
NAAC, MHRD
9th
RANK(INDIA):
NIRF 2017
150+
INTERNATIONAL
PARTNERS