This book discusses various vision systems, their classifications and design strategies. It is essential reading for academic and industrial researchers to understand the computational intelligence research domain as related to vision systems and their applications.

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 extremely versatile, and this book focusses on the latest developments and current key research areas in the field.

Key Features:

• Interdisciplinary approach to intelligent computing applications for machine vision

• Encompasses high performance computing for vision systems and control

• Includes present applications and challenges for future development

• Reviews range of CI and ML methodologies

• International author pool

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