Symbolic Visual Learning

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ISBN 10: 0195098706 / ISBN 13: 9780195098709

Published by OXFORD UNIV PR, 1997

About this Item

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

Bibliographic Details

• Title: Symbolic Visual Learning
• Publisher: OXFORD UNIV PR
• Publication Date: 1997
• Language: English
• ISBN 10: 0195098706
• ISBN 13: 9780195098709
• Binding: Hardcover
• Condition: Sehr gut
• Seller catalogs:
  • Bücher

About this title

Synopsis

Some of the fundamental constraints of automated machine vision have been the inability to automatically 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.

About the Author

Katsuchi Ikeuchi is at Carnegie Mellon University. Manuela Velosa is at Carnegie Mellon University.