Computer Vision and Recognition Systems: Research Innovations and Trends

Description

This cutting-edge volume, Computer Vision and Recognition Systems: Research Innovations and Trends, focuses on how artificial intelligence can be used to give computers the ability to imitate human sight. It explains the essential modules that are necessary for comprehending artificial intelligence experiences to provide machines with the power of vision.With contributions from researchers in diverse countries, including Thailand, Spain, Japan, Turkey, Australia, and India, the book discusses how machine learning and deep learning are important aspects in computer vision and recognition systems. The volume also presents a number of innovative research developments, applications, and current trends in the field. The chapters cover such topics as visual quality improvement, Parkinson’s disease diagnosis, hypertensive retinopathy detection through retinal fundus, big image data processing, N-grams for image classification, medical brain images, chatbot applications, credit score improvisation, vision-based lane and vehicle detection, damaged vehicle parts recognition, partial image encryption of medical images, and image synthesis. The chapter authors show different approaches to computer vision, image processing, and frameworks for machine learning to build automated and stable applications. Deep learning is included for making immersive application-based systems, pattern recognition, and biometric systems. The book also considers efficiency and comparison at various levels of using algorithms for real-time applications, processes, and analysis.This volume provides a rich source of information for researchers, professionals, and anyone working in computer vision and recognition systems. Cover Half Title Title Page Copyright Page About the Editors Table of Contents Contributors Abbreviations Preface 1. Visual Quality Improvement Using Single Image Defogging Technique 2. A Comparative Study of Machine Learning Algorithms in Parkinson’s Disease Diagnosis: A Review 3. Machine Learning Algorithms for Hypertensive Retinopathy Detection through Retinal Fundus Images 4. Big Image Data Processing: Methods, Technologies, and Implementation Issues 5. N-grams for Image Classification and Retrieval 6. A Survey on Evolutionary Algorithms for Medical Brain Images 7. Chatbot Application with Scene Graph in Thai Language 8. Credit Score Improvisation through Automating the Extraction of Sentiment from Reviews 9. Vision-Based Lane and Vehicle Detection: A First Step Toward Autonomous Unmanned Vehicle 10. Damaged Vehicle Parts Recognition Using Capsule Neural Network 11. Partial Image Encryption of Medical Images Based on Various Permutation Techniques 12. Image Synthesis with Generative Adversarial Networks (GAN) Index