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Advanced methods and deep learning in computer vision

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advanced methods and deep learning in computer visionedited by E.R. Davies, Matthew A. Turk.
其他作者:	Davies, E. R.
出版者:	London, United Kingdom ;Elsevier Academic Press,2022.
面頁冊數:	1 online resource (xix, 562 p.) :ill. (black and white, and col.)
標題:	Computer vision.
電子資源:	https://www.sciencedirect.com/science/book/9780128221099
ISBN:	9780128221495$q(ebook)

Advanced methods and deep learning in computer vision
Advanced methods and deep learning in computer vision[electronic resource] /edited by E.R. Davies, Matthew A. Turk. - London, United Kingdom ;Elsevier Academic Press,2022. - 1 online resource (xix, 562 p.) :ill. (black and white, and col.) - Computer vision and pattern recognition.

Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5-10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection. This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as a textbook for a second course on computer vision and deep learning for advanced undergraduates and graduate students.

ISBN: 9780128221495$q(ebook)Subjects--Topical Terms:

200113
Computer vision.
Index Terms--Genre/Form:

214472
Electronic books.

LC Class. No.: TA1634 / .A38 2022eb

Dewey Class. No.: 006.37

Advanced methods and deep learning in computer vision
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520 $a Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5-10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection. This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as a textbook for a second course on computer vision and deep learning for advanced undergraduates and graduate students.
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650 0 $a Pattern recognition systems. $3 183725
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