Challenges and Applications for Implementing Machine Learning in Computer Vision

ISBN 10: 1799801837 / ISBN 13: 9781799801832

Edité par IGI Global, US, 2019

A propos de cet article

Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing. There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques.

N° de réf. du vendeur LU-9781799801832

Détails bibliographiques

• Titre: Challenges and Applications for Implementing Machine Learning in Computer Vision
• Éditeur: IGI Global, US
• Date de parution: 2019
• Langue: anglais
• ISBN à 10 chiffres: 1799801837
• ISBN à 13 chiffres: 9781799801832
• Reliure: Paperback
• État de l'article: New

A propos de ce titre

Synopsis

Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing. There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques.

À propos des auteurs

Ramgopal Kashyap has areas of interest in image processing, pattern recognition, and machine learning. He has published many research papers in international journals and conferences like Springer, Inderscience, Elsevier, ACM, and IGI-Global indexed by Science Citation Index (SCI) and Scopus (Elsevier) and many book chapters. He has Reviewed Research Papers in the Science Citation Index Expanded, Springer Journals and Editorial Board Member and conferences programme committee member of the IEEE, Springer international conferences and journals held in countries: Czech Republic, Switzerland, UAE, Australia, Hungary, Poland, Taiwan, Denmark, India, USA, UK, Austria, and Turkey. He has written many book chapters published by Springer, Elsevier and IGI Global, USA.

A. V. Senthil Kumar is working as a Director & Professor in the Department of Research and PG in Computer Applications, Hindusthan College of Arts and Science, Coimbatore. He has finished is Doctor of Science during February 2023. He has more than 26 years of teaching experience and 5 years of Industry experience. He has to his credit 30 Book Chapters, 220 papers in International and National Journals, 55 papers in International and National Conferences, and 10 edited books and 2 text books. He is an Editor-in-Chief for various journals. Key Member for India, Machine Intelligence Research Lab (MIR Labs).He is Associate Editor of IEEE Access. He is the first person in South India to receive the highest degree in academic field D.Sc in Computer Science.