Intelligent Machine Learning in Computer Vision: What, Why, and How?
Papers from the 1993 Fall SymposiumSymposium
Kevin Bowyer and Lawrence Hall, Cochairs
Technical Report FS-93-04. Published by The AAAI Press, Menlo Park, California
This technical report is available in book format.
Please Note: Abstracts are linked to individual titles, and will appear in a separate browser window. Full-text versions of the papers are linked to the abstract text. Access to full text may be restricted to AAAI members. PDF file sizes may be large!
Contents
Incremental Modelbase Updating: Learning New Model Sites
Kuntal Sengupta and Kim L. Boyer, The Ohio State University / 1
Learning Image to Symbol Conversion
Malini Bhandaru, Bruce Draper and Victor Lesser, University of Massachusetts at Amherst / 6
Transformation-invariant Indexing and Machine Discovery for Computer Vision
Darrell Conklin, Queen’s University / 10
Recognition and Learning of Unknown Objects in a Hierarchical Knowledge-base
L. Dey, P. P. Das, and S. Chaudhury, I.I.T., Delhi / 15
Unsupervised Learning of Object Models
C. K. I. Williams, R. S. Zemel, Univ. of Toronto; M. C. Mozer, Univ. of Colorado / 20
Learning and Recognition of 3-D Objects from Brightness Images
Hiroshi Murase and Shree K. Nayar, Columbia University / 25
Adaptive Image Segmentation Using Multi-Objective Evaluation and Hybrid Search Methods
Bir Bhanu, Sungkee Lee, Subhodev Das, University of California / 30
Learning 3D Object Recognition Models from 2D Images
Arthur R. Pope and David G. Lowe, University of British Columbia / 35
Matching and Clustering: Two Steps Towards Automatic Objective Model Generation
Patric Gros, LIFIA, Grenoble, France / 40
Learning About A Scene Using an Active Vision System
P. Remagnino, M. Bober and J. Kittler, University of Surrey, UK / 45
Learning Indexing Functions for 3-D Model-Based Object Recognition
Jeffrey S. Beis and David G. Lowe, University of British Columbia / 50
Non-Accidental Features in Learning
Richard Mann and Allan Jepson, University of Toronto / 55
Feature-Based Recognition of Objects
Paul A. Viola, Massachusetts Institute of Technology / 60
Learning Correspondences Between Visual Features and Functional Features
Hitoshi Matsubara, Katsuhiko Sakaue and Kazuhiko Yamamoto, ETL, Japan / 65
A Self-Organizing Neural Network that Learns to Detect and Represent Visual Depth from Occlusion Events
Johnathon A. Marshall and Richard K. Alley, University of North Carolina / 70
Learning from the Schema Learning System
Bruce Draper, University of Massachusetts / 75
Learning Symbolic Names for Perceived Colors
J. M. Lammens and S. C. Shapiro, SUNY Buffalo / 80
Extracting a Domain Theory from Natural Language to Construct a Knowledge Base for Visual Recognition
Lawrence Chachere and Thierry Pun, University of Geneva / 85
A Vision-Based Learning Method for Pushing Manipulation
Marcos Salganicoff, Univ. of Pennsylvania; Giorgio Metta, Andrea Oddera and Giulio Sandini, University of Genoa. / 90
A Classifier System for Learning Spatial Representations Based on a Morphological Wave Propagation Algorithm
Michael M. Skolnick, R.P.I. / 95
Evolvable Modeling: Structural Adaptation Through Hierarchical Evolution for 3-D Model-based Vision
Thang C. Nguyen, David E. Goldberg, Thomas S. Huang, University of Illinois / 100
Developing Population Codes for Object Instantiation Parameters
Richard S. Zemel, Geoffrey E. Hinton, University of Toronto / 105
Integration of Machine Learning and Vision into an Active Agent Paradigm
Peter W. Pachowicz, George Mason University / 110
Assembly Plan from Observation
K. Ikeuchi and S. B. Kang, Carnegie-Mellon University / 115
Learning Shape Models for a Vision Based Human-Computer Interface
Jakub Segen, A.T.& T. Bell Laboratories / 120
Learning Visual Speech
G. J. Wolff, K. V. Prasad, D. G. Stork & M. Hennecke, Ricoh California Research Center / 125
Learning Open Loop Control of Complex Motor Tasks
Jeff Schneider, University of Rochester / 130
Issues in Learning from Noisy Sensory Data
J. Bala and P. Pachowicz, George Mason University / 135
Learning Combination of Evidence Functions in Object Recognition
D. Cook, L. Hall, L. Stark and K. Bowyer, University of South Florida / 139
Learning to Eliminate Background Effects in Object Recognition
Robin R. Murphy, Colorado School of Mines / 144
The Prax Approach to Learning a Large Number of Texture Concepts
J. Bala, R. Michalski, and J. Wnek, George Mason University / 148
Non-Intrusive Gaze Tracking Using Artificial Neural Networks
Dean A. Pomerleau and Shumeet Baluja, Carnegie Mellon University / 153
Toward a General Solution to the Symbol Grounding Problem: Combining Learning and Computer Vision
Paul Davidsson, Lund University / 157
Symbolic and Subsymbolic Learning for Vision: Some Possibilities
Vasant Honavar, Iowa State University / 161