Screen LibraryComputer 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 intelli...
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| Group Author: | ; ; |
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| Published: |
Apple Academic Press,
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| Publisher Address: | Palm Bay, FL, USA : |
| Publication Dates: | 2022. |
| Literature type: | Book |
| Language: | English |
| Edition: | First edition. |
| Subjects: | |
| Summary: |
"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."-- |
| Carrier Form: | xiv, 256 pages : illustrations (some color), forms ; 24 cm |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: |
9781774630150 177463015X |
| Index Number: | TA1634 |
| CLC: |
TP18 TP391.4 |
| Call Number: | TP391.4/C738-3 |
| Contents: | Visual Quality Improvement Using Single Image Defogging Technique / Pritam Verma and Vijay Kumar -- A Comparative Study of Machine Learning Algorithms in Parkinson's Disease Diagnosis: A Review / Pedram Khatamino and Zeynep Orman -- Machine Learning Algorithms for Hypertensive Retinopathy Detection through Retinal Fundus Images / N. Jagan Mohan, R. Murugan, and Tripti Goel -- Big Image Data Processing: Methods, Technologies, and Implementation Issues / U. S. N. Raju, Suresh Kumar Kanaparthi, Mahesh Kumar Morampudi, Sweta Panigrahi, and Debanjan Pathak -- N-grams for Image Classification and Retrieval / Pradnya S. Kulkarni -- A Survey on Evolutionary Algorithms for Medical Brain Images / Nurşah Dincer and Zeynep Orman -- Chatbot Application with Scene Graph in Thai Language / Chantana Chantrapornchai and Panida Khuphira -- Credit Score Improvisation through Automating the Extraction of Sentiment from Reviews / Aadit Vikas Malikayil, Maheswari R., Azath H., and Sharmila P. -- Vision-Based Lane and Vehicle Detection: A First Step Toward Autonomous Unmanned Vehicle / Tapan Kumar Das -- Damaged Vehicle Parts Recognition Using Capsule Neural Network / Kundjanasith Thonglek, Norawit Urailertprasert, Patchara Pattiyathanee, and Chantana Chantrapornchai -- Partial Image Encryption of Medical Images Based on Various Permutation Techniques / Kiran, B. D. Parameshachari, H. T. Panduranga, and Rocío Pérez de Prado -- Image Synthesis with Generative Adversarial Networks (GAN) / Parvathi R. and Pattabiraman V. |