nach der Bestellung gedruckt Neuware - Printed after ordering - Recent advancements in imaging techniques and image analysis has broadened the horizons for their applications in various domains. Image analysis has become an influential technique in medical image analysis, optical character recognition, geology, remote sensing, and more. However, analysis of images under constrained and unconstrained environments require efficient representation of the data and complex models for accurate interpretation and classification of data. Deep learning methods, with their hierarchical/multilayered architecture, allow the systems to learn complex mathematical models to provide improved performance in the required task. The Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments provides a critical examination of the latest advancements, developments, methods, systems, futuristic approaches, and algorithms for image analysis and addresses its challenges. Highlighting concepts, methods, and tools including convolutional neural networks, edge enhancement, image segmentation, machine learning, and image processing, the book is an essential and comprehensive reference work for engineers, academicians, researchers, and students.

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Detalles bibliogr�ficos

T�tulo: Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments
Editor: Engineering Science Reference
Fecha de publicaci�n: 2020
Idioma: Ingl�s
ISBN 10: 1799866904
ISBN 13: 9781799866909
Encuadernaci�n: Buch
Estado: Neu

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Sinopsis

Recent advancements in imaging techniques and image analysis has broadened the horizons for their applications in various domains. Image analysis has become an influential technique in medical image analysis, optical character recognition, geology, remote sensing, and more. However, analysis of images under constrained and unconstrained environments require efficient representation of the data and complex models for accurate interpretation and classification of data. Deep learning methods, with their hierarchical/multilayered architecture, allow the systems to learn complex mathematical models to provide improved performance in the required task. The Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments provides a critical examination of the latest advancements, developments, methods, systems, futuristic approaches, and algorithms for image analysis and addresses its challenges. Highlighting concepts, methods, and tools including convolutional neural networks, edge enhancement, image segmentation, machine learning, and image processing, the book is an essential and comprehensive reference work for engineers, academicians, researchers, and students.

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Vijayalakshmi G. V. Mahesh received her BE in Electronics and Communication Engineering from Bangalore University, India in 1999, and M.Tech in Digital Communication and Networking from Visvesvaraya Technological University in 2005 and the Ph.D. degree from the Vellore Institute of Technology, Vellore, India. Currently she is working as an Associate Professor at BMS Institute of Technology and Management, Bangalore, India. She has been in academics for over 19 years and has published her research in various reputed journals and conferences. Dr. Vijayalakshmi is serving as academic editor for various journals. She has edited and published two books " Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments" and " Aiding Forensic Investigation Through Deep Learning and Machine Learning Frameworks" with IGI Global publishers. Her research interests include Machine Learning, Image Processing, Pattern Recognition and Deep learning, Affective computing.

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