Symbolic Visual Learning
Published on 29. May 1997
Book
Hardback
368 pages
978-0-19-509870-9 (ISBN)
Description
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.
More details
Language
English
Place of publication
New York
United States
Target group
College/higher education
Professional and scholarly
Illustrations
halftone and line figures, tables
Dimensions
Height: 260 mm
Width: 183 mm
Thickness: 24 mm
Weight
879 gr
ISBN-13
978-0-19-509870-9 (9780195098709)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Content
1. The Visual Learning Problem ; 2. MULTI-HASH: Learning Object Attributes and Hash Tables for Fast 3D Object Recognition ; 3. Learning Control Strategies for Object Recognition ; 4. PADO: A New Learning Architecture for Object Recognition ; 5. Learning Organization Hierarchies of Large Modelbases for Fast Recognition ; 6. Application of Machine Learning in Function-Based Recognition ; 7. Learning a Visual Model and an Image Processing Strategy from a Series of Silhouette Images on MIRACLE-IV ; 8. Assembly Plan from Observation ; 9. Visual Event Perception ; 10. A Knowledge Framework for Seeing and Learning ; 11. Explanation Based Learning for Mobile Robot Perception ; 12. Navigation with Landmarks: Computing Goal Locations from Place Codes