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Symbolic Visual Learning by Katsushi Ikeuchi (School of Computer Science, School of Computer Science, Carnegie Mellon University) at Abbey's Bookshop,
Symbolic Visual Learning
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Katsushi Ikeuchi (School of Computer Science, School of Computer Science, Carnegie Mellon University) Manuela Velosa (School of Computer Science, School of Computer Science, Carnegie Mellon University)

Symbolic Visual Learning

Katsushi Ikeuchi (School of Computer Science, School of Computer Science, Carnegie Mellon University),  Manuela Velosa (School of Computer Science, School of Computer Science, Carnegie Mellon University),  Manuela Velosa

$371

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English
Oxford University Press Inc
01 January 1997
Neural networks & fuzzy systems; Computer vision
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.
Edited by:   , , , ,
Imprint:   Oxford University Press Inc
Country of Publication:   United States
Dimensions:   Height: 263mm,  Width: 185mm,  Spine: 22mm
Weight:   825g
ISBN:   9780195098709
ISBN 10:   0195098706
Pages:   364
Publication Date:  
Audience:   College/higher education ,  Professional and scholarly ,  Professional & Vocational ,  A / AS level ,  Further / Higher Education
Format:   Hardback
Publisher's Status:   Active
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

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