Smart Computer Vision

This book addresses and disseminates research and development in the applications of intelligent techniques for computer vision, the field that works on enabling computers to see, identify, and process images in the same way that human vision does, and then providing appropriate output. The book provides contributions which include theory, case studies, and intelligent techniques pertaining to computer vision applications. The book helps readers grasp the essence of the recent advances in this complex field. The audience includes researchers, professionals, practitioners, and students from academia and industry who work in this interdisciplinary field. The authors aim to inspire future research both from theoretical and practical viewpoints to spur further advances in the field.

Introduction to theoretical foundations and practical solution techniques for computer vision applications Includes a variety of case studies emphasizing social and research perspectives in computer vision Features contributors form industry, academia, and researchers with a variety of perspectives

Auteur
B.Vinoth Kumar is working as an Associate Professor with 18 years of experience in the Department of Information Technology at PSG College of Technology. His current research interests include Computational Intelligence, Memetic algorithms, Affective computing and Image Processing. He is the author of more than 60 papers in refereed Journals and International conferences. He has edited six books with reputed publishers such as Springer and CRC Press. He serves as a Guest Editor/Reviewer of many journals with leading publishers such as Springer, Inderscience and De Gruyter.

P. Siva Kumar is working as an Assistant Professor with 11 years of experience at PSG College of Technology. His current research interests include IoT, Image Processing, Model Based Design and Testing of Automotive software, AUTOSAR, AGLinux. He has edited three books with reputed publishers such as Springer and CRC Press. He has published papers in peer reviewed National/International Journals and Conferences and a Reviewer of International Journals. He serves as a Guest Editor/Reviewer of many journals with leading publishers such as Inderscience.

B. Surendiran is currently working as Associate Professor in Department of Computer Science and Engineering at National Institute of Technology Puducherry, Karaikal. He had completed his Ph.D from National Institute of Technology Trichy. He has more than 45 publications in international conferences and Journals. He had reviewed more than 400+ papers for various journals and conferences. His research interests include medical imaging, machine learning, dimensionality reduction, intrusion detection systems.

Junhua Ding is working as a Professor at University of North Texas, USA. His primary research focus is on Intelligent Systems and Big Data Analytics, particularly as applied to development of clinical diagnostic systems and biomedical informatics. His current research projects and interests include Data Analytics, Management and Quality Assurance of Big Data, Intelligent Bioinformatics, Computer Science, Data Science, and Software Engineering Education.

Contenu
Chapter 1: A Systematic Review on Machine Learning based Sports Video Summarization Techniques.- Chapter 2: Shot Boundary Detection from Lecture Video Sequences using Histogram of Oriented Gradients and Radiometric Correlation.- Chapter 3: Detection of Road Potholes using Computer Vision and Machine Learning Approaches to Assist the Visually Challenged.- Chapter 4: Shape Feature Extraction Techniques for Computer Vision Applications.- Chapter 5: GLCM Feature Based Texture image classification using Machine learning algorithms.- Chapter 6: Progress in Multimodal Affective Computing: From Machine Learning to Deep Learning.- Chapter 7: Content based Image Retrieval using Deep features and Hamming Distance.- Chapter 8: Bio Inspired CNN approach for diagnosing COVID-19 using images of Chest X-ray.- Chapter 9: Initial Stage Identification of Covid-19 using Capsule Networks.- Chapter 10: Deep Learning in Auto Encoder Framework and Shape Prior for Hand Gesture Recognition.- Chapter 11: Hierarchical based Semantic segmentation of 3D point cloud using deep learning.- Chapter 12: Convolution Neural Network and Auto-Encoder hybrid scheme for Automatic Colorization of Gray-Scale images.- Chapter 13: Deep learning based Open Set Domain Hyper spectral Image Classification using dimension reduced spectral features.- Chapter 14: An Effective Diabetic Retinopathy Detection using Hybrid Convolutional Neural Network Models.- Chapter 15: Modified Discrete Differential Evolution with Neighbourhood Approach for Grayscale Image Enhancement.- Chapter 16: Swarm-based methods applied to computer vision. <p