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.
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.
Dr A.V.Senthil Kumar obtained his BSc Degree (Physics) in 1987, P.G.Diploma in Computer Applications in 1988, MCA in 1991 from Bharathiar University. He obtained his Master of Philosophy in Computer Science from Bharathidasan University, Trichy during 2005 and his Ph.D in Computer Science from Vinayaka Missions University during 2009. To his credit he had industrial experience for five years as System Analyst in a Garment Export Company. Later he took up teaching and attached to CMS College of Science and Commerce, Coimbatore. He has to his credit 3 Book Chapters, 8 papers in International Journals, 2 papers in National Journals, 13 papers in International Conferences, 4 papers in National Conferences, and edited a book in Data Mining (IGI Global, USA) and a book in Mobile Computing (IGI Global, USA). He is an Editor-in-Chief for International Journal titled International Journal of Data Mining and Emerging Technologies, International Journal of Image Processing and Applications and International Journal of Advances in Knowledge Engineering & Computer Science. Key Member for India, Machine Intelligence Research Lab (MIR Labs). He is an Editorial Board Member and Reviewer for various International Journals. He is also a Committee member for various International Conferences. He is a Life member of International Association of Engineers (IAENG), Systems Society of India (SSI), member of The Indian Science Congress Association, member of Internet Society (ISOC), International Association of Computer Science and Information Technology (IACSIT), Indian Association for Research in Computing Science (IARCS), and committee member for various International Conferences. He has got many awards from National and International Societies. Also a freelance writer for Tamil Computer (a fortnightly) and PC Friend (monthly).
|
Ask a Question About this Product More... |
|
