Synopsis
Some of the fundamental constraints of automated machine vision have been the inability automatically to adapt parameter settings or utilize previous adaptations in changing environments. Symbolic Visual Learning presents research which adds visual learning capabilities to computer vision systems. Using this state-of-the-art recognition technology, the outcome is different adaptive recognition systems that can measure their own performance, learn from their experience and outperform conventional static designs. Written as a companion volume to Early Visual Learning (edited by S. Nayar and T. Poggio), this book is intended for researchers and students in machine vision and machine learning.
"synopsis" may belong to another edition of this title.
Synopsis
Some of the fundamental constraints of automated machine vision have been the inability automatically to adapt parameter settings or utilize previous adaptations in changing environments. This work presents research which adds visual learning capabilities to computer vision systems. Using this state-of-the-art recognition technology, the outcome is different adaptive recognition systems that can measure their own performance, learn from their experience and outperform conventional static designs. The book was written as a companion volume to "Early Visual Learning".
"About this title" may belong to another edition of this title.
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Symbolic Visual Learning
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Oxford University Press, 1997
ISBN 10: 0195098706
ISBN 13: 9780195098709
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Symbolic Visual Learning
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Symbolic Visual Learning
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ISBN 13: 9780195098709
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Condition: Sehr gut. Zustand: Sehr gut | Seiten: 368 | Sprache: Englisch | Produktart: Bücher | Some of the fundamental constraints of automated machine vision have been the inability automatically to adapt parameter settings or utilize previous adaptations in changing environments. Symbolic Visual Learning, as presented in this book, consists of an area of research that tries to overcome these fundamental constraints, enhancing state-of-the-art recognition systems that can measure their own performance, learn from their experience, and outperform conventional static designs. It was written as a companion volume to Early Visual Learning edited by S. Nayar and T. Poggio. Seller Inventory # 10050240/202
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