Computational Intelligence Based Solutions for Vision Systems
Description
Computer vision and image processing-based systems and their applications are already an integral part of modern living and are expected to increase in prevalence and complexity. Vision system provides the ability to handle and examine the large data generated by cameras and make a decision based on the situational requirement. As computational intelligent methods are especially adept at rapidly resolving inexact situations or where there is incomplete knowledge, they are being heavily researched and employed in this space. This merger creates intelligent vision systems, which can be used for smart surveillance, medical science and healthcare, remote sensing, target detection and tracking, industrial processes monitoring, quality assurance, autonomous vehicles and more. Considering the current research boundaries and future applications of high-performance computing and the need of intelligent vision systems, this book focusses on the latest developments and current key research areas in the field. With an example led approach, it will be essential reading for academic and industrial researchers to understand the computational intelligence research domain as related to vision systems and their applications.
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Persons
Irshad Ahmad Ansari has been working as a faculty in the discipline of Electronics and Communication Engineering, at Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Jabalpur, India since 2017. He completed his PhD at IIT Roorkee and subsequently joined Gwangju Institute of Science and Technology, South Korea as a Postdoctoral fellow. His major research interest includes Image Processing, Signal Processing, Soft Computing, Data Classification, Brain Computer Interface.
Varun Bajaj (PhD, SMIEEE) is a faculty member at the Electronics and Communication Engineering department at the Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Jabalpur. Prior to this he worked as a visiting faculty in IIITDM Jabalpur and Assistant Professor at Department of Electronics and Instrumentation, Shri Vaishnav Institute of Technology and Science, Indore, India. He received B.E. degree in Electronics and Communication Engineering from Rajiv Gandhi Technological University, Bhopal, India in 2006, M.Tech. Degree with Honors in Microelectronics and VLSI design from Shri Govindram Seksaria Institute of Technology & Science, Indore, India in 2009. He received his Ph.D. degree in the Discipline of Electrical Engineering, at Indian Institute of Technology Indore, India in 2014. He is an Associate Editor of IEEE Sensor Journal and Subject Editor-in-Chief of IET Electronics Letters. He also served as a Subject Editor of IET Electronics Letters. He is Senior Member of the IEEE and also contributes as active technical reviewer of leading International journals of IEEE, IET, and Elsevier, etc. He has authored numerous research papers and edited several book projects. His research interests include biomedical signal processing, image processing, time frequency analysis, and computer-aided medical diagnosis.
Content
Chapter 1: Drone based Vision System: Surveillance during Calamities
Chapter 2: Use of computer vision to inspect automatically machined workpieces
Chapter 3: Machine learning for vision based crowd management
Chapter 4: Skin cancer classification model based on hybrid deep feature generation and
iterative mRMR
Chapter 5: An analysis of human activity recognition systems and its importance in the
current era
Chapter 6: A Deep Learning Based Food Detection and Classification
Chapter 7: Detection of Images Recaptured Through Screenshot Based on Spatial Rich Model
Analysis
Chapter 8: Data augmentation for deep ensembles in polyp segmentation
Chapter 9: Identification of the onset of Parkinson's disease through a multiscale classification
deep learning model utilizing a fusion of multiple conventional features with nDS-spatially exploited symmetrical convolutional pattern
Chapter 10: Computer Vision Approach With Deep Learning for Medical Intelligence System
Chapter 11: Machine Learning in medicine: Diagnosis of skin cancer using Support Vector
Machine (SVM) Classifier
