Computer Vision and Recognition Systems : Research Innovations and Trends

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. This cutting-edge volume focuses on how artificial intelligence can be used to give computers the ability to imitate human sight. With contributions from researchers in diverse countries, including Thailand, Spain, Japan, Turkey, Australia, and India, the book explains the essential modules that are necessary for comprehending artificial intelligence experiences to provide machines with the power of vision. The volume also presents 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 vehicle lane 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. Focuses on how artificial intelligence can be used to give computers the ability to imitate human sight. Topics include Parkinson’s disease diagnosis, big image data processing, N-grams for image classification, medical brain images, credit score improvisation, vision-based lane and vehicle detection, damaged vehicle parts recognition, partial imag Cover 1 Half Title 2 Title Page 4 Copyright Page 5 About the Editors 6 Table of Contents 8 Contributors 10 Abbreviations 12 Preface 16 1. Visual Quality Improvement Using Single Image Defogging Technique 18 2. A Comparative Study of Machine Learning Algorithms in Parkinson’s Disease Diagnosis: A Review 30 3. Machine Learning Algorithms for Hypertensive Retinopathy Detection through Retinal Fundus Images 56 4. Big Image Data Processing: Methods, Technologies, and Implementation Issues 86 5. N-grams for Image Classification and Retrieval 110 6. A Survey on Evolutionary Algorithms for Medical Brain Images 138 7. Chatbot Application with Scene Graph in Thai Language 166 8. Credit Score Improvisation through Automating the Extraction of Sentiment from Reviews 182 9. Vision-Based Lane and Vehicle Detection: A First Step Toward Autonomous Unmanned Vehicle 200 10. Damaged Vehicle Parts Recognition Using Capsule Neural Network 214 11. Partial Image Encryption of Medical Images Based on Various Permutation Techniques 240 12. Image Synthesis with Generative Adversarial Networks (GAN) 256 Index 268 big,image,data,processing;,credit,score,improvisation;,defogging,technique;,generative,adversarial,network;,image,synthesis;,hypertensive,retinopathy,detection;,machine,learning,algorithm;,medical,brain,image;,Parkinson’s,disease,diagnosis;,permutation,technique;,vision-based,lane,and,vehicle,detection;,visual,quality big image data processing,credit score improvisation,defogging technique,generative adversarial network,image synthesis,hypertensive retinopathy detection,machine learning algorithm,medical brain image,Parkinson’s disease diagnosis,permutation technique,vision-based lane and vehicle detection,visual quality "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."-- Provided by publisher