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

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

Sponsored Project

Professional Achievements

  • Recipient of Best Session Award for organizing the Special Session on “Artificial Intelligence for Smart Healthcare” in the World Conference in Virtual Format on “Congress on Intelligent Systems (CIS-2020) conducted by Soft Computing Research Society in association with Springer Publications during September 05 - 06, 2020.
  • Received "Women In AI Leadership Awards 2019 (Only Academician among the winners)" sponsored by Jigsaw Academy, during the one day Conference, "The Rising 2019" on Women in Analytics and AI, organized by the Analytics India on March 8, 2019 at Taj Hotel, Bangalore.
  • Awarded with Deep Learning Instructor Ambassadorship Grant by NVIDIA (December 2018).
  • Invited Speaker by Premier National Institutes to deliver lectures on “Deep Learning and its Applications in Image Processing”.
  • Organizer of the “Special Session on Artificial Intelligence and Machine Learning Applications (AIML)”, in the Scopus Indexed Springer International Conferences.
  • Recipient of several National Awards for Teaching and Research.
  • Author of more than 100 Scopus Indexed Publications in the area of Machine Learning and Deep Learning applied to Signal and Image Analysis, attained through M.Tech Projects Guidance.
  • International Level Collaborator with University of Wyoming in the research area of “Image Analysis and Drones”.

Webinars rendered during COVID 2020-2021

  • Session on “AI in Computer Vision” organized by Technology Enabling Center (TEC), Amrita Vishwa Vidyapeetham on February 27, 2021.
  • Session on “Constrained Optimization and Support Vector Machines” in the AICTE sponsored Online FDP on “Machine Learning and Optimization” organized by Ahalia School of Engineering and Technology, Kerala on February 24, 2021.
  • Session on “AI for Biomedical Image Analysis” in the ATAL sponsored Online FDP organized by Department of Biomedical Engineering, Model Engineering College, Kerala on February 22, 2021.
  • Session on “Drone Image Analysis Using Deep Learning for Agricultural Applications” in 5 - Days AICTE - ATAL Sponsored FDP on “Artificial Intelligence, Machine Learning and Deep Learning”, organized by Department of ECE, Karpagam College of Engineering, Coimbatore on February 5, 2021.
  • Session on “AI in Signal Processing” in the one week AICTE sponsored STTP on “Artificial Intelligence” organized by Department of ECE, Sona College of Technology, Salem on January 6, 2021.
  • Session on “AI for Healthcare” in the AICTE Sponsored STTP on “Leveraging Artificial Intelligence and Data Analytics for Helathcare” organized by K.J.Somaiya Institute of Engineering and Information Technology, Mumbai on January 4, 2021.
  • Session on “Artificial Intelligence and Deep Learning” in the FDP, organized by Department of ECE, Kalasalingam Academy of Research and Education on December 31, 2020.
  • Session on “Concepts of Data Science” in the AICTE Sponsored STTP on “IOT and DataScience”, organized by Department of ECE, St.Joseph’s College of Engineering and Technology, Keral on December 28, 2020.
  • Session on “Supervised Deep Learning Based Approaches for Image Analysis” in the AICTE sponsored 6 days STTP on Supervised and Unsupervised Machine Learning Using Google Cloud, organized by Department of ECE, R.M.K.College of Engineering and Technology, Chennai on November 21, 2020.
  • Session on “Efficacy of Artificial Intelligence”, in “HER TALKS – The Virtual Meetup”, organized by the software development company: Informatica on November 19, 2020.
  • Session on “Biomedical Processing” in the 5 - Days AICTE - ATAL Sponsored FDP on Data Sciences, organized by Department of Information Technology, National Institute of Technology Karnataka (NIT-K), Surathkal on September 25, 2020.
  • Session on “Artificial Intelligence for HealthCare” in the Short Term Training Program (STTP) on “Biomedical Systems Design using Artificial Intelligence & Machine Learning” sponsored by AICTE, Delhi and organized by Biomedical Engineering and Technology Incubation Center, Nagpur and Department of Artificial Intelligence, G.H.Raisoni College of Engineering, Nagpur on September 9, 2020.
  • Session on “Efficacy of Artificial Intelligence” hosted by the Department of Computer Science, and Engineering, KMEA engineering College, Kerala in association with Computer Society of India on June 8, 2020.
  • Session on “Expert View on Artificial Intelligence and Data Science (Courses after 12th exam)” hosted by Shiksha.com belongs to Info Edge (India) Ltd, the owner of established brands like Naukri.com, 99acres.com, Jeevansathi.com on May 15, 2020.
  • Session on “Efficacy of Artificial Intelligence” hosted by the Department of Mathematics with Computer Applications, Shri Nehru Maha Vidyalaya College of arts and Sciences and Institute of Management, affiliated to Bharathiar University, Coimbatore on May 6, 2020.

Outcome of Internal Seed Funding

Received the internal seed fund (IFRP_4) which paved the way for International Collaboration with University of Wyoming. This progressed to the joint International Conference Publication in the American Society for Photogrammetry and Remote sensing (ASPRS) held at Denver, 2019. [Link]

Professional Experience

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

Research Area

  • Artificial Intelligence for HealthCare Analytics
  • Smart Agriculture Applications Using Drones
  • Color Image Processing
  • Hyperspectral Image Processing
  • Pattern Classification
  • Machine Learning
  • Deep Learning
  • Bio-Medical Signal Processing
  • Bio-Medical Image Processing
  • Image Analysis using Drones

Active Reviewer in the following SCI Indexed Journals

  1. ISPRS Journal of Photogrammetry and Remote Sensing (Elsevier).
  2. Computers and Electronics in Agriculture (Elsevier).
  3. Computer and Information Sciences (Elsevier).
  4. Neural Networks (Elsevier).
  5. Remote Sensing Letters (Taylor & Francis).
  6. Signal, Image and Video Processing (Springer).
  7. Digital Signal Processing (Elsevier).
  8. International Journal of Image and Data Fusion.
  9. Applied Soft Computing (ASOC).
  10. Information Sciences.
  11. Journal of King Saud University, Computer and Information Sciences (JKSUCI).

Invited Talks

  • Rendered one-day invited lecture on "Generative Adversarial Networks (GAN) for Computer Vision" as a part of the TEQIP-III sponsored Workshop on "Artificial Intelligence and Machine Learning Applications in the Emerging Areas of Computer Science and Information Technology " held at NIT-Suratkal on December 13, 2019.
  • Rendered one-day invited lecture on "Applications of Deep Learning in Image Processing Applications" as a part of the FDP program on "Deep Learning and Machine Learning Approaches and its Applications" held at NIT - Calicut on December 12, 2019.
  • Rendered a session on "Basic theory of Deep Neural and Convolutional Network" during "Two days workshop on Machine Learning - Hands on with Matlab and Python" , organized by Center for Development Advanced Computing (C-DAC), Trivandrum during 5-6 July, 2019.
  • Rendered a session on "Fundamentals of Computer Vision using NVIDIA DIGITS " (as a part of NVIDIA DLI University Ambassador Grant) during "CSIR sponsored National Level Seminar on Deep Learning" , organized by P.A.College of Engineering and Technology, Pollachi on June 26, 2019.
  • Rendered a session on "Fundamentals of Computer Vision using NVIDIA DIGITS " (as a part of NVIDIA DLI University Ambassador Grant) during "FDP on Deep Learning for Object Detection", organized by Sona College of Technology, Salem on June 21, 2019.
  • Rendered a session on "Generative Adversarial Networks (GAN)", along with the hands on in python at a National Level Faculty Development Program on "Deep Learning Unfolded", conducted by Amrita School of Engineering, Amritapuri Campus, Kollam on May 31, 2019.
  • Rendered a session on "Drones for Forestry Applications" at a monthly seminar conducted by the Institute of Forest Genetics and Tree Breeding (IFGTB), Indian Council of Forestry Research and Education, Coimbatore, India on May 30, 2019.
  • Delivered one day workshop on “Fundamentals of Deep Learning for Computer Vision – Hands-on using NVIDIA DIGITS” at KMEA College of Engineering, Aluva on May 14, 2019. This was certified and sponsored by NVIDIA as a part of “NVIDIA Deep Learning Instructor University Ambassadorship Award”.
  • Delivered a guest lecture on “Opportunities in Remote Sensing” at Avinashilingam Institute of Home Science and Higher Education for Women, Coimbatore on March 6, 2019.

@ NIT-Suratkal, Karnataka Invitation

@ NIT - Calicut Momento

@ CDAC-Trivandrum

  • Delivered one-day workshop on "Computational Tools Needed for Data Science (with hands-on in Matlab and Python)" during the 9th National Level Tech fest - Anokha 2019 organized by Amrita School of Engineering, Coimbatore during February 14-16, 2019.
  • Delivered a guest lecture on “Machine Learning” at Karpagam College of Engineering, Coimbatore on January 24, 2019.
  • Invited talk on "Deep Learning" in Two days IEEE workshop on Machine Learning held at Kalasalingam University on 2-3 Feb, 2018.
  • Invited talk on "Deep Learning for Bio-medical Application" in ICMR Sponsored Seminar on Deep Learning Techniqies and Tools for Medical Application organized by Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi on 17/01/2018.
  • Delivered a lecture on “Deep Learning for Medical Image Processing” in “TEQIP Sponsored Artificial Intelligence for Biomedical Applications” organized by TKM College of Engineering, Kollam, on 14th Dec, 2017.
  • Delivered a lecture on “Drone based Hyperspectral Imaging for Precision Agriculture” in “Refresher Course for Computer Science” organized by Bharathiar University, on 21st Nov, 2017.
  • Delivered a lecture on “Data Mining” for MBA students of Avinashilingam University on 18th March 2017.
  • Delivered a session on “Least square based image processing” as a part of short term training programme on Digital Signal Processing and its Applications held at Govt.Engineering College, Thrissur on 5th Dec, 2016.
  • Rendered hands on training in “Support Vector Machines using Libsvm and Weka” for M.Tech students of the Department of Electronics and Communication Engineering, Rajiv Gandhi Institute of Technology, Kottayam on 18-12-2015.
  • Delivered one day session on ‘PDE and Image Processing” in two days “National Level Workshop on Signal and Image Processing” conducted by Department of Information Technology, Sona College of technology, Salem during 4-5 Dec, 2015.
  • Rendered a lecture on “PDE based Image Processing” in “A training workshop on Advanced Optimization, Deep Learning Applications (AODA)” organized by organized by the Centre for Excellence in Computational Engineering and Networking (CEN), Amrita Vishwa Vidyapeetham (University), Coimbatore, during January 30 – February 2, 2014.
  • Rendered Invited Guest Lecture on “Remote Sensing and Applications of GIS” at Avinashilingam University, Coimbatore, 2012.
  • Rendered a talk on “Hyperspectral Image Processing” at “First National Workshop on “Sparse Image and Signal Processing (SISP-2011)”, organized by the Centre for Excellence in Computational Engineering and Networking (CEN), Amrita Vishwa Vidyapeetham (University), Coimbatore.

Awards

  • Awarded "Bharat Excellence" and "Best Indian Global Personalities"  by Friendship Forum of India at Delhi on July 28, 2019.
  • Awarded "Best Engineering College Teacher" by Society for Engineering Education Enrichment (SEEE) at Dr.N.G.P Institute of Technology, Coimbatore on July 20, 2019.
  • Awarded with "Excellence in Research for the academic year 2016-2017" by Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham on 23rd Institution day held on 10th January 2018.
  • Awarded with "Best Young Researcher Award" rendered by Integrated Intelligent Research Groups on the occasion of Republic Day Achievers award 2018, celebrated at Loyola-ICAM College of Engineering and Technology (LICET), Chennai.
  • Awarded with PG Merit Scholarship for University Rank Holders - Rs.40000 by University Grants Commission (UGC), New-Delhi in the year 2008.
  • Awarded with “Young Women Educator and Researcher” by National Foundation for Entrepreneurship Development (NFED), Coimbatore on 4th Women’s Day Awards celebrated on 8th March 2017 at Grand Reagent Coimbatore.
  • Awarded with “Young Women Achiever (in recognition of Your Role, Outstanding Contributions, Significant Achievements and Sustained Excellence in the field of Engineering) of the Women Awards - VIWA 2016” celebrated on 5 March 2016 at Radha Regent Chennai.
  • Received third prize in Essay Competition conducted by Amrita nature club on the occasion of International Women’s Day on March 8th, 2014.
  • Awarded with title “Associate of the month of January 2011” for project excellence by Cognizant Technology Solutions (CTS), Chennai.
  • Awarded with shield for securing University First Rank in M.TECH (Remote Sensing and Wireless Sensor Networks) (2008-2010) by Amrita Vishwa Vidyapeetham, Coimbatore.
  • Awarded with Gold Medal for University First Rank in B.Sc., (Physics) (2003-2006) by Avinashilingam University, Coimbatore.
  • Awarded with cash award of Rs.5000 for securing First Place in the paper presented in National Level Seminar on Signal Processing held at Sree NarayanaGuru Institute of Science and technology, Kochi.
  • Awarded with “Swami Vivekananda Award” for Excellence in Education by Yuva Kendra Association, Madurai.

Professional Activities

  • Program Committee Member, Reviewer and Technical Session Chair on “Inteliigent System: Algortihms and Applications” in the Scopus Indexed Springer International Conference on Communication and Computational Technologies (ICCCT), organized by Rajasthan Technical University in association with Soft Computing Research Scoiety (SCRS) held online during February 27-28, 2021.
  • Program Committee Member, Reviewer and Technical Session Chair on “Inteliigent System: Algortihms and Applications” in the Scopus Indexed Springer International Conference on Communications and Intelligent Systems (ICCIS), organized by Rajasthan Technical University in association with Soft Computing Research Scoiety (SCRS) held online during December 26-27, 2020.
  • Organizer of the Special Session titled “Artificial Intelligence and Machine Learning (AIML)” in the Scopus Indexed Springer 4th  International Conference on Smart Computing and Informatics (SCI), organized by Vasavi College of Engineering, Hyderabad during October 9-10, 2020.
  • Organizer of the Special Session titled “Advanced Deep Learning Methods for Multidisciplinary Applications (ADMMA) in the Scopus Indexed International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), organized by Reva University, Bangalore during October 9-10, 2020.
  • Organizer of the Special Session titled “Artificial Intelligence and Machine Learning (AIML)” in the Scopus Indexed Springer 4th  International Conference on Intelligent Computing and Coomunication (ICICC), organized by Dayananda Sagar University, Bangalore during September 18-20, 2020.
  • Chaired the technical session titled, “Artificial Intelligence”, in the World Conference in Virtual Format on “Congress on Intelligent Systems (CIS-2020)" conducted by Soft Computing Research Society in association with Springer Publications during September  05 - 06, 2020.
  • Program Committee Member and Reviewer in the World Conference in Virtual Format on “Congress on Intelligent Systems (CIS-2020) "conducted by Soft Computing Research Society in association with Springer Publications during September 05 - 06, 2020.
  • Chaired a Technical Session on Machine Learning and Deep Learning Applications as a part of Main Track in "8th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2020)" held at NIT-K, Surathkal on 4-5 Jan 2020.
  • One of the organizers of "Special Session on Artificial Intelligence and Machine Learning Applications (AIML)" as a part of SCOPUS INDEXED SPRINGER International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA), January 4-5, 2020 at NIT Suratkal, Karnataka.
  • Participated in the hands-on ZOYC workshop on "Automation of Image Processing & Analysis using Cloud Infrastructure", organized by AIMS, Cochin and Carl Zeiss India (Bangalore) Pvt.Ltd on 22 April, 2019.
  • Actively Participated in the "Accenture Learning Symposium workshops on Deep Learning and DevOps", conducted on March 13-14, 2018 at Amrita School of Engineering, Coimbatore.
  • Successfully cleared the assessment test and completed a hands on workshop on "Artificial Intelligence and Deep Learning," held at Kongu Engineering College, Erode from July 28 - 30, 2018, conducted by leadingIndia.ai, a nation wide initiative by Bennett University, Greater Noida, India. (Listed as one of the toppers in the assessment)
  • Presented a paper tiled, "Inspiring stories from Indian Freedom Movement" in SADGAMAYA 5119, Cultural Camp organized by Amrita Vishwa Vidyapeetham, Coimbatore on 29-30 June 2017.
  • Event Coordinator - ‘Cook without Fire or Wire’ of Amrita Cultural Fest “Amritotsavam-2015’.
  • Participated in one day Seminar on “Projects in Signal & Image Processing, Communication, embedded, Robotics, Networks and VLSI”, organized by Department of Electronics and Communication Engineering, SNS College of Engineering, Coimbatore on 20th July, 2013.
  • Participated in Faculty Development programme on “Linear Algebra and Applications” organized by the department of Mathematics and Centre for Continuing Education held at National Institute of Technology, Calicut during 07-13 July 2013.
  • Participated in National Workshop on “Computer Vision and Image Processing Techniques” organized by Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham, Coimbatore on 15-16 March 2013.
  • Participated in two days workshop on “Geospatial Technologies for Coastal Resources Management”, organized by Department of Earth and Space Sciences, Indian Institute of Space Science and Technology (IIST), Thiruvananthapuram on 19-20 May 2012.
  • Participated in two days workshop on “Machine Vision and Image Processing using Labview”, organized by Department of Instrumentation and Control Systems Engineering, PSG College of Technology, in association with NI Systems (India) Pvt.Ltd, Bangalore on 18-19 May 2012.
  • Participated in “Second Edition of Amrita International Conference of Women in Computing” organized by Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham, Coimbatore on 9-11 January 2012.
  • Co-ordinator of the “First National Workshop on “Sparse Image and Signal Processing (SISP-2011)”, organized by the Centre for Excellence in Computational Engineering and Networking (CEN), Amrita Vishwa Vidyapeetham, Coimbatore during 23rd Dec-26th Dec,2011.

Presentations

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

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.

Webinars Delivered During COVID 2020-2021

  • Session on “AI in Computer Vision” organized by Technology Enabling Center (TEC), Amrita Vishwa Vidyapeetham on February 27, 2021.
  • Session on “Constrained Optimization and Support Vector Machines” in the AICTE sponsored Online FDP on “Machine Learning and Optimization” organized by Ahalia School of Engineering and Technology, Kerala on February 24, 2021.
  • Session on “AI for Biomedical Image Analysis” in the ATAL sponsored Online FDP organized by Department of Biomedical Engineering, Model Engineering College, Kerala on February 22, 2021.
  • Session on “Drone Image Analysis Using Deep Learning for Agricultural Applications” in 5 - Days AICTE - ATAL Sponsored FDP on “Artificial Intelligence, Machine Learning and Deep Learning”, organized by Department of ECE, Karpagam College of Engineering, Coimbatore on February 5, 2021.
  • Session on “AI in Signal Processing” in the one week AICTE sponsored STTP on “Artificial Intelligence” organized by Department of ECE, Sona College of Technology, Salem on January 6, 2021.
  • Session on “AI for Healthcare” in the AICTE Sponsored STTP on “Leveraging Artificial Intelligence and Data Analytics for Helathcare” organized by K.J.Somaiya Institute of Engineering and Information Technology, Mumbai on January 4, 2021.
  • Session on “Artificial Intelligence and Deep Learning” in the FDP, organized by Department of ECE, Kalasalingam Academy of Research and Education on December 31, 2020.
  • Session on “Concepts of Data Science” in the AICTE Sponsored STTP on “IOT and DataScience”, organized by Department of ECE, St.Joseph’s College of Engineering and Technology, Keral on December 28, 2020.
  • Session on “Supervised Deep Learning Based Approaches for Image Analysis” in the AICTE sponsored 6 days STTP on Supervised and Unsupervised Machine Learning Using Google Cloud, organized by Department of ECE, R.M.K.College of Engineering and Technology, Chennai on November 21, 2020.
  • Session on “Efficacy of Artificial Intelligence”, in “HER TALKS – The Virtual Meetup”, organized by the software development company: Informatica on November 19, 2020.
  • Session on “Biomedical Processing” in the 5 - Days AICTE - ATAL Sponsored FDP on Data Sciences, organized by Department of Information Technology, National Institute of Technology Karnataka (NIT-K), Surathkal on September 25, 2020.

Selected

Publications

Publication Type: Book Chapter

Year of Publication Title

2020

P. Gopika, Sowmya V., Gopalakrishnan, E. A., and Dr. Soman K. P., “Transferable Approach for Cardiac Disease Classification using Deep Learning”, in Deep Learning Techniques for Biomedical and Health Informatics, B. Agarwal, Balas, V. Emilia, Jain, L. C., Poonia, R. Chandra, and Manisha,, Eds. Academic Press, 2020, pp. 285-303, Academic Press.[Abstract]


Cardiovascular disease is a condition that causes damage to the heart muscle, valves, rhythm, or blockage in the blood vessels. It requires early diagnosis, as it is the leading cause for the sudden death in humans. Electrocardiogram (ECG) is the most important biomedical signal used extensively by the cardiologist to diagnose cardiovascular disease. The classification of ECG signals helps physicians to make decisions in the diagnosis of cardiac diseases. There are many conventional machine learning and deep learning algorithms used in the literature for the automatic classification of ECG signals. Conventional machine learning algorithms require handcrafted features. There are many features such as morphological feature extraction, computation of RR interval, QRS peak detection, ST segment, ST distance, and amplitude computation. The classical machine learning algorithms used to classify the extracted features are shallow neural network, K nearest neighbor, support vector machines (SVM), random forest, and decision tree. Deep learning algorithms learn the features from the given training data, which outperformed the handcrafted features used in the conventional machine learning algorithms. There are state-of-the-art architectures such as convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU). The existing architectures rely on disease-specific approach. This chapter aims to provide a single architecture by transferable approach for cardiac disease classification using ECG. We plan to achieve this by the analysis of the different state-of-the-art deep learning architectures such as CNN, LSTM, RNN, and GRU to classify cardiac diseases using ECG signal. Arrhythmia, myocardial infarction, and atrial fibrillation are the cardiac diseases we considered for the study. We have used publically available datasets from Kaggle and PhysioNet for experimental evaluation. In the proposed methodology, the deep learning architectures such as RNN, LSTM, and GRU trained to classify the atrial fibrillation from the ECG signal were used to classify other cardiac diseases such as arrhythmia and myocardial infarction. Similarly, CNN, which is trained to classify arrhythmia and myocardial infarction, is used to classify atrial fibrillation. We kept the network parameters (also known as hyperparameters) such as learning rate, batch size, and number of epochs the same for the entire experimental analysis. We evaluated the performance of the proposed methodology using the metrics: precision, recall, and F1 score. We observed that LSTM and GRU performed well compared to the RNN and CNN. LSTM and GRU offer the maximum precision and recall score, which varies between 0.97–0.98 for all three diseases. The computational cost of GRU is less compared to the RNN. Our results show that the deep learning architectures are transferable. Unlike deep learning, which is the data-driven approach, the machine learning algorithms are not adaptable or transferable. To validate this, comparison between the different machine learning algorithms like Naive Bayes, K-nearest neighbor, SVMs with two different kernels such as linear and RBF, AdaBoost, random forest, decision tree, and logistic regression are conducted for all three diseases considered for the study.

More »»

2020

P. Gopika, Krishnendu, C. S., M. Chandana, H., Ananthakrishnan, S., Sowmya V., Gopalakrishnan, E. A., and Soman, K. P., “Single-layer Convolution Neural Network for Cardiac Disease Classification using Electrocardiogram Signals”, in Deep Learning for Data Analytics, H. Das, Pradhan, C., and Dey, N., Eds. Academic Press, 2020, pp. 21-35, Academic Press.[Abstract]


Medical diagnosis is the process of determining a patient’s health condition by the observation of symptoms and test results. Cardiovascular diseases are one of the most common causes of death worldwide. Electrocardiogram (ECG) is one of the effective ways for diagnosing heart conditions. ECG records and detects the strength and timing of electrical activity of the heart. A proper diagnosis can reduce mortality rate. Artificial intelligence (AI) has shown its inexplicable contribution in the field of medical science, especially in diagnosis. Convolutional neural network (CNN) is the most popular deep learning algorithm, which captures the relevant features by itself. Deep learning requires a massive amount of data to train the network, which increases the computational complexity. This chapter aims to reduce the computational complexity. We consider the cardiac diseases such as arrhythmia and myocardial infarction (MI) for our experimental analysis. We have used heartbeat segmented and preprocessed ECG data available at Kaggle. We aim to reduce the computational complexity of the existing deep learning architecture for cardiac disease classification by using the feature-extracted data. We propose the single-layer CNN for the classification of ECG beats of arrhythmia and MI. We also evaluated the performance of the proposed model by using the following evaluation metrics: precision, recall, and F1 score. The performance of the proposed architecture is high compared to state-of-the-art methods.

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2020

N. Harini, Ramji, B., Sriram, S., Sowmya V., and Soman, K. P., “Musculoskeletal Radiographs Classification using Deep Learning”, in Deep Learning for Data Analytics, H. Das, Pradhan, C., and Dey, N., Eds. Academic Press, 2020, pp. 79-98, Academic Press.[Abstract]


Worldwide 1.7 billion people suffer from various musculoskeletal conditions and it leads to severe disability and long-term pain. Due to the lack of limited qualified radiologists in various parts of the world, there is a need for an automatic framework that can accurately detect abnormalities in the radiograph images. Deep learning (DL) is very popular due to its capability of extracting useful features automatically with less human intervention, and it is used for solving various research problems in a wide range of fields like biomedical, cybersecurity, autonomous vehicles, etc. The convolutional neural network (CNN) based models are especially used in many biomedical applications because CNN is capable of automatic extraction of the location-invariant features from the input images. In this chapter, we look at the effectiveness of various CNN-based pretrained models for detecting abnormalities in radiographic images and compare their performances using standard statistical measures. We will also analyze the performance of pretrained CNN architectures with respect to radiographic images on different regions of the body and discuss in detail the challenges of the data set. Standard CNN networks such as Xception, Inception v3, VGG-19, DenseNet, and MobileNet models are trained on radiograph images taken from the musculoskeletal radiographs (MURA) data set, which is given as an open challenge by Stanford machine learning (ML) group. It is the large data set of MURA that contains 40,561 images from 14,863 studies (9045 normal and 5818 abnormal studied) which represents various parts of the body such as the elbow, finger, forearm, hand, humerus, shoulder, and wrist. In this chapter, finger, wrist, and shoulder radiographs are considered for binary classification (normal, abnormal) due to the fact that data from these categories are less biased (less data imbalance) when compared to other categories. There are in total 23,241 and 1683 images given as train and valid set in this data set for the three categories considered in the present work. In the experimental analysis, the performance of the models are measured using statistical measures such as accuracy, precision, recall and F1-score.

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2020

K. Radhika, Devika, K., Aswathi, T., Sreevidya, P., Sowmya V., and Dr. Soman K. P., “Performance Analysis of NASNet on Unconstrained Ear Recognition”, in Nature Inspired Computing for Data Science, Springer International Publishing, 2020, pp. 57-82, Springer, Cham.[Abstract]


Recent times are witnessing greater influence of Artificial Intelligence (AI) on identification of subjects based on biometrics. Traditional biometric recognition algorithms, which were constrained by their data acquisition methods, are now giving way to data collected in the unconstrained manner. Practically, the data can be exposed to factors like varying environmental conditions, image quality, pose, image clutter and background changes. Our research is focused on the biometric recognition, through identification of the subject from the ear. The images for the same are collected in an unconstrained manner. The advancements in deep neural network can be sighted as the main reason for such a quantum leap. The primary challenge of the present work is the selection of appropriate deep learning architecture for unconstrained ear recognition. Therefore the performance analysis of various pretrained networks such as VGGNet, Inception Net, ResNet, Mobile Net and NASNet is attempted here. The third challenge we addressed is to optimize the computational resources by reducing the number of learnable parameters while reducing the number of operations. Optimization of selected cells as in NASNet architecture is a paradigm shift in this regard.

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2019

A. Simon, Vinayakumar, R., Sowmya V., Soman, K. Padannayil, and Gopalakrishnan, E. Anathanara, “A Deep Learning Approach for Patch-based Disease Diagnosis from Microscopic Images”, in Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis, N. Dey, Ed. Academic Press, 2019, pp. 109-127, Academic Press.[Abstract]


Disease diagnosis classifies patients’ health conditions into specific grades and helps to make appropriate decisions for better treatment. Advancement in the field of microscopy and computer vision enables efficient disease diagnosis, a requirement for economical healthcare. Recently, deep learning recasts the face of computer vision, which outperforms humans in object recognition tasks. Usually, convolutional neural networks (CNNs) are used for image recognition tasks due to the fact that such a network architecture considers the spatial structure of the images. Instead of depending on CNN alone, here we introduce a new architecture, which consists of a shallow CNN appended with a single recurrent layer. Performance comparison of the proposed architectures on microscopic images have been done by using three different types of recurrent layers, such as recurrent neural network, long short term memory, and gated recurrent unit. We also evaluated the performance of all these models on three different disease diagnosis tasks from microscopic images: tuberculosis in sputum samples, intestinal parasite eggs in stool samples, and malaria in thick blood smears. In all cases, the proposed models produce better performance than state-of-the-art models. The proposed deep architectures for disease diagnosis fewer trainable parameters when compared to the existing state-of-the-art deep architecture.

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2019

N. Damodaran, Sowmya V., Govind, D., and Dr. Soman K. P., “Scene Classification using transfer Learning”, in Studies in Computational Intelligence, vol. 804, Springer Verlag, 2019, pp. 363-399, Springer, Cham.[Abstract]


Categorization of scene images is considered as a challenging prospect due to the fact that different classes of scene images often share similar image statistics. This chapter presents a transfer learning based approach for scene classification. A pre-trained Convolutional Neural Network (CNN) is used as a feature extractor for the images. The pre-trained network along with classifiers such as Support Vector Machines (SVM) or Multi Layer Perceptron (MLP) are used to classify the images. Also, the effect of single plane images such as, RGB2Gray, SVD Decolorized and Modified SVD decolorized images are analysed based on classification accuracy, class-wise precision, recall, F1-score and equal error rate (EER). The classification experiment for SVM was also done using a dimensionality reduction technique known as principal component analysis (PCA) on the feature vector. By comparing the results of models trained on RGB images with those grayscale images, the difference in the results is very small. These grayscale images were capable of retaining the required shape and texture information from the original RGB images and were also sufficient to categorize the classes of the given scene images. © Springer Nature Switzerland AG 2019.

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2019

Sowmya V., Dr. Soman K. P., and Hassaballah, M., “Hyperspectral image: Fundamentals and advances”, in Studies in Computational Intelligence, vol. 804, Springer Verlag, 2019, pp. 401-424, Springer, Cham.[Abstract]


Hyperspectral remote sensing has received considerable interest in recent years for a variety of industrial applications including urban mapping, precision agriculture, environmental monitoring, and military surveillance as well as computer vision applications. It can capture hyperspectral image (HSI) with a lager number of land-cover information. With the increasing industrial demand in using HSI, there is a must for more efficient and effective methods and data analysis techniques that can deal with the vast data volume of hyperspectral imagery. The main goal of this chapter is to provide the overview of fundamentals and advances in hyperspectral images. The hyperspectral image enhancement, denoising and restoration, classical classification techniques and the most recently popular classification algorithm are discussed with more details. Besides, the standard hyperspectral datasets used for the research purposes are covered in this chapter. © Springer Nature Switzerland AG 2019.

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Publication Type: Journal Article

Year of Publication Title

2020

Chippy Jayaprakash, Damodaran, B. Bhushan, Sowmya V., and Dr. Soman K. P., “Dimensionality Reduction Methods for Hyperspectral Image Classification”, Journal of Applied Remote Sensing , vol. 14, no. 3, pp. 036507 (IF: 1.370, CiteScore: 2.6, Q2- 69 percentile), 2020.[Abstract]


High-dimensional features often cause computational complexity and dimensionality curse. Feature selection and feature extraction are the two mainstream methods for dimensionality reduction. Feature selection but not feature extraction can preserve the critical information and maintain the physical meaning simultaneously. Herein, we proposed a dimensionality reduction method based on rough set theory (DRM-RST) for feature selection. We defined the hyperspectral image as a decision system, extracted the features as decision attributes, and selected the effective features based on information entropy. We used the Washington D.C. Mall dataset and New York dataset to evaluate the performance of DRM-RST on dimensionality reduction. Compared with full band classification, 184 or 185 redundant bands were removed in DRM-RST, respectively. DRM-RST achieved similar accuracy (overall accuracy >94%) by SVM classifier and reduced computing time by about 85%. We further compared the dimensionality reduction efficiency of DRM-RST against other popular methods, including ReliefF, Sequential Backward Elimination (SBE) and Information Gain (IG). The Producer’s accuracy (PA) and User’s accuracy (UA) of DRM-RST was greater than that of ReliefF and IG. DRM-RST showed greater stability of accuracy than SBE in dimensionality reduction when using for different datasets. Collectively, this study provides a new method for dimensionality reduction that can reduce computational complexity and alleviate dimensionality curse.

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2020

Sowmya V., Sanjana, K., Gopalakrishnan, E. A., and Dr. Soman K. P., “Explainable Artificial Intelligence for Heart Rate Variability in ECG Sgnal”, Healthcare Technology Letters, vol. 7, no. 6, pp. 146 (IF:1.157, CiteScore: 3.1, Q2- 64 percentile), 2020.[Abstract]


Electrocardiogram (ECG) signal is one of the most reliable methods to analyse the cardiovascular system. In the literature, there are different deep learning architectures proposed to detect various types of tachycardia diseases, such as atrial fibrillation, ventricular fibrillation, and sinus tachycardia. Even though all types of tachycardia diseases have fast beat rhythm as the common characteristic feature, existing deep learning architectures are trained with the corresponding disease-specific features. Most of the proposed works lack the interpretation and understanding of the results obtained. Hence, the objective of this letter is to explore the features learned by the deep learning models. For the detection of the different types of tachycardia diseases, the authors used a transfer learning approach. In this method, the model is trained with one of the tachycardia diseases called atrial fibrillation and tested with other tachycardia diseases, such as ventricular fibrillation and sinus tachycardia. The analysis was done using different deep learning models, such as RNN, LSTM, GRU, CNN, and RSCNN. RNN achieved an accuracy of 96.47% for atrial fibrillation data set, 90.88% accuracy for CU-ventricular tachycardia data set, and also achieved an accuracy of 94.71, and 94.18% for MIT-BIH malignant ventricular ectopy database for ECG lead I and lead II, respectively. The RNN model could only achieve an accuracy of 23.73% for the sinus tachycardia data set. A similar trend is shown by other models. From the analysis, it was evident that even though tachycardia diseases have fast beat rhythm as their common feature, the model was not able to detect different types of tachycardia diseases. The deep learning model could only detect atrial fibrillation and ventricular fibrillation and failed in the case of sinus tachycardia. From the analysis, they were able to interpret that, along with the fast beat rhythm, the model has learned the absence of P-wave which is a common feature for ventricular fibrillation and atrial fibrillation but sinus tachycardia disease has an upright positive P-wave. The time-based analysis is conducted to find the time complexity of the models. The analysis conveyed that RNN and RSCNN models could achieve better performance with lesser time complexity.

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2019

A. Unnikrishnan, Sowmya V., and Dr. Soman K. P., “Deep learning architectures for land cover classification using red and near-infrared satellite images”, Multimedia Tools and Applications, vol. 78, no. 13, pp. 18379-18394 (IF: 2.313, CiteScore: 3.7, Q1- 80 percentile)., 2019.[Abstract]


Classification of remotely sensed data requires the modelling of suitable image processing algorithms. The rise of machine learning systems upgraded the viability of satellite image applications. Using Convolutional Neural Networks (CNN), benchmark classification exactness can be accomplished for land cover grouping. Motivated by the concept of Normalized Difference Vegetation Index (NDVI), this paper utilizes only the red and near infrared (NIR) band information for classifying the publicly available SAT-4 and SAT-6 datasets. This is done, since NDVI computation requires only the two band (red and NIR) information and the classes involved in the dataset are types of vegetation. In this work, new deep learning architectures for three different networks (AlexNet, ConvNet, VGG) were proposed by hypertuning the network and the input as two band data. The modified architectures with the two band information along with reduced number of filters were trained and tested model manages to classify the images into different classes. The proposed architectures are compared against the existing architectures in terms of accuracy, precision and trainable parameters. The proposed architecture is found to perform equally efficient by retaining high accuracy with less number of trainable parameters, when compared against the the performance of benchmark deep learning architectures for satellite image classification. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

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2019

Sowmya V., Govind, D., and Dr. Soman K. P., “Significance of Processing Chrominance Information for Scene Classification: a Review”, Artificial Intelligence Review, vol. 53, no. 2, pp. 811-842 (IF: 5.747, CiteScore: 9.1, Q1- 89 percentile), 2019.[Abstract]


The primary objective of this paper is to provide a detailed review of various works showing the role of processing chrominance information for color-to-grayscale conversion. The usefulness of perceptually improved color-to-grayscale converted images for scene classification is then studied as a part of this presented work. Various issues identified for the color-to-grayscale conversion and improved scene classification are presented in this paper. The review provided in this paper includes, review on existing feature extraction techniques for scene classification, various existing scene classification systems, different methods available in the literature for color-to-grayscale image conversion, benchmark datasets for scene classification and color-to-gray-scale image conversion, subjective evaluation and objective quality assessments for image decolorization. In the present work, a scene classification system is proposed using the pre-trained convolutional neural network and Support Vector Machines developed utilizing the grayscale images converted by the image decolorization methods. The experimental analysis on Oliva Torralba scene dataset shows that the color-to-grayscale image conversion technique has a positive impact on the performance of scene classification systems. © 2019, Springer Nature B.V.

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2018

R. Reshma, Sowmya V., and Dr. Soman K. P., “Effect of Legendre–Fenchel denoising and SVD-based Dimensionality Reduction Algorithm on Hyperspectral image Classification”, Neural Computing and Applications, vol. 29, pp. 301-310 (IF: 4.774, CiteScore: 6.5, Q1- 81 percentile), 2018.[Abstract]


This paper describes the importance of performing preprocessing techniques namely, denoising and dimensionality reduction to the hyperspectral data before classification. The two main problems faced in hyperspectral image processing are noise and higher dimension. Legendre–Fenchel transformation denoises each band in the data while preserving the edge information. To overcome the issue of high data volume, inter-band block correlation coefficient technique followed by singular value decomposition and QR decomposition is utilized to reduce the dimension of hyperspectral image without affecting the critical information. The preprocessed data are classified using kernel-based libraries, namely GURLS and LibSVM. Performance of these techniques is evaluated with accuracy assessment measures. The experiment was performed on five datasets. Experimental analysis shows that the proposed denoising technique increases the classification accuracy. In the case of Indian Pines data, with 10% of the training data, the classification accuracy is improved from 83.5 to 97.3%. And also, dimensionality reduction technique gives good classification accuracy even with 50% reduction in the number of bands. The classification accuracy of the Salinas-A and Pavia University data is 99.4 and 94.6% with the 50% dimensionally reduced (100 and 50 bands, respectively) number of bands. The bands extracted by the dimensionality reduction technique using the denoised hyperspectral data differ from that of the hyperspectral data without denoising. This emphasizes the importance of denoising the dataset before applying dimensionality reduction technique. In case of Pavia University, the band numbers above 50 (out of 100 bands) which were not informative bands before denoising are selected as informative bands by dimension reduction technique after denoising. © 2017, The Natural Computing Applications Forum.

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2018

M. Swarna, Sowmya V., and Dr. Soman K. P., “Band selection using variational mode decomposition applied in sparsity-based hyperspectral unmixing algorithms”, Signal, Image and Video Processing, vol. 12, no. 8, pp. 1463-1470 (IF: 1.794, CiteScore: 3.8, Q2- 69 percentile), 2018.[Abstract]


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

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2018

V. Ankarao, Sowmya V., and Dr. Soman K. P., “Multi-sensor Data Fusion using NIHS Transform and Decomposition Algorithms”, Multimedia Tools and Applications, vol. 77, pp. 30381–30402 (IF: 2.313, CiteScore: 3.7, Q1- 80 percentile), 2018.[Abstract]


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

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2017

Sowmya V., Divakaran, G., and Dr. Soman K. P., “Significance of perceptually relevant image decolorization for scene classification”, Journal of Electronic ImagingJournal of Electronic Imaging, vol. 26, no. 6, pp. 063019 (IF: 0.780, CiteScore: 2.0, Q3- 46 percentile), 2017.[Abstract]


Color images contain luminance and chrominance components representing the intensity and color information, respectively. The objective of this paper is to show the significance of incorporating chrominance information to the task of scene classification. An improved color-to-grayscale image conversion algorithm that effectively incorporates chrominance information is proposed using the color-to-gray structure similarity index and singular value decomposition to improve the perceptual quality of the converted grayscale images. The experimental results based on an image quality assessment for image decolorization and its success rate (using the Cadik and COLOR250 datasets) show that the proposed image decolorization technique performs better than eight existing benchmark algorithms for image decolorization. In the second part of the paper, the effectiveness of incorporating the chrominance component for scene classification tasks is demonstrated using a deep belief network-based image classification system developed using dense scale-invariant feature transforms. The amount of chrominance information incorporated into the proposed image decolorization technique is confirmed with the improvement to the overall scene classification accuracy. Moreover, the overall scene classification performance improved by combining the models obtained using the proposed method and conventional decolorization methods.

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2017

Sowmya V., Govind, D., and Dr. Soman K. P., “Significance of Incorporating Chrominance Information for Effective color-to-grayscale Image Conversion”, Signal, Image and Video Processing, vol. 11, no. 1, pp. 129–136 (IF: 1.794, CiteScore: 3.8, Q2- 69 percentile), 2017.[Abstract]


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

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