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Computer Vision and Recognition Systems Using Machine and Deep Learning Approaches: Fundamentals, Technologies and Applications

Language EnglishEnglish
Book Hardback
Book Computer Vision and Recognition Systems Using Machine and Deep Learning Approaches: Fundamentals, Technologies and Applications Mamoun Alazab
Libristo code: 36278676
Publishers INSTITUTION OF ENGINEERING & T, December 2021
Computer vision is an interdisciplinary scientific field that deals with how computers obtain, store... Full description
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Computer vision is an interdisciplinary scientific field that deals with how computers obtain, store, interpret and understand digital images or videos using artificial intelligence based on neural networks, machine learning and deep learning methodologies. They are used in countless applications such as image retrieval and classification, driving and transport monitoring, medical diagnostics and aerial monitoring.

Written by a team of international experts, this edited book covers the state-of-the-art of advanced research in the fields of computer vision and recognition systems from fundamental concepts to methodologies and technologies and real world applications including object detection, biometrics, Deepfake detection, sentiment and emotion analysis, traffic enforcement camera monitoring, vehicle control and aerial remote sensing imagery.

The book will be useful for industry and academic researchers, scientists and engineers in the fields of computer vision, machine vision, image processing and recognition, multimedia, AI, machine and deep learning, data science, biometrics, security, and signal processing. It will also make a great course reference for advanced students and lecturers in these fields of research.

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About the book

Full name Computer Vision and Recognition Systems Using Machine and Deep Learning Approaches: Fundamentals, Technologies and Applications
Language English
Binding Book - Hardback
Date of issue 2021
Number of pages 506
EAN 9781839533235
ISBN 1839533234
Libristo code 36278676
Weight 975
Dimensions 163 x 241 x 30
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