Sinossi
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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Contenuti
- 1: K. Ikeuchi and M. Veloso: The Visual Learning Problem
- 2: L. Grewe and A. Kak: MULTI-HASH: Learning Object Attributes and Hash Tables for Fast 3D Object Recognition
- 3: B.A. Draper: Learning Control Strategies for Object Recognition
- 4: A. Teller and M. Veloso: PADO: A New Learning Architecture for Object Recognition
- 5: K.L. Boyer and K. Sengupta: Learning Organization Hierarchies of Large Modelbases for Fast Recognition
- 6: L. Stark et al.: Application of Machine Learning in Function-Based Recognition
- 7: H. Matsubara, K. Sakaue and K. Yamamoto: Learning a Visual Model and an Image Processing Strategy from a Series of Silhouette Images on MIRACLE-IV
- 8: K. Ikeuchi, T. Suehiro and S.B. Kang: Assembly Plan from Observation
- 9: J.M. Siskind: Visual Event Perception
- 10: P.R. Cooper and M.A. Brand: A Knowledge Framework for Seeing and Learning
- 11: J. O'Sullivan, T.M. Mitchell and S. Thrun: Explanation Based Learning for Mobile Robot Perception
- 12: A. Redish and D.S. Touretzky: Navigation with Landmarks: Computing Goal Locations from Place Codes
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Symbolic Visual Learning
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Oxford University Press, 1997
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Condizione: very good. New York & Oxford : Oxford University Press, 1997, Hardcover. Viii, 355p : ill ; 27cm. Companion volume to: Early visual learning. Includes bibliographical references and index. - 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. Condition : very good copy. ISBN 9780195098709. Keywords : PSYCHOLOGY, Codice articolo 204494
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Condizione: 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. Codice articolo 10050240/202
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