From honeydew.srv.cs.cmu.edu!das-news.harvard.edu!noc.near.net!howland.reston.ans.net!gatech!usenet.ufl.edu!suntan.eng.usf.edu!waterfall!hall Fri Sep 10 15:27:34 EDT 1993 Article: 18768 of comp.ai Xref: honeydew.srv.cs.cmu.edu comp.ai:18768 comp.ai.neural-nets:12249 comp.ai.fuzzy:1125 Path: honeydew.srv.cs.cmu.edu!das-news.harvard.edu!noc.near.net!howland.reston.ans.net!gatech!usenet.ufl.edu!suntan.eng.usf.edu!waterfall!hall From: hall@waterfall.csee.usf.edu (Larry Hall) Newsgroups: comp.ai,comp.ai.neural-nets,comp.ai.fuzzy Subject: AAAI Fall Symposium on Machine Learning in Computer Vision Final Program Date: 10 Sep 1993 16:08:55 GMT Organization: University of South Florida, Department of Computer Science and Engineering Lines: 177 Sender: hall@waterfall (Larry Hall) Distribution: world Message-ID: <26q8qn$cog@suntan.eng.usf.edu> NNTP-Posting-Host: waterfall.csee.usf.edu AAAI Fall Symposium Series Machine Learning in Computer Vision: What, Why and How? Final Program October 22 - 24, 1993 Sheraton Imperial Hotel and Convention Center Research Triangle Park Raleigh, North Carolina (registration is limited-- e-mail fss@aaai.org for registration information) Friday, October 22 9:00 - 10:30-- Exciting and Controversial Invited talks on Learning and Vision Task-Oriented Vision Learning, Tom Mitchell, Carnegie-Mellon University In what sense might vision be learned?, Chris Brown, University of Rochester 11:00 - 12:30-- moderated by Diane Cook, University of Texas at Arlington Incremental Modelbase Updating: Learning New Model Sites Kuntal Sengupta and Kim L. Boyer, The Ohio State University Learning Image to Symbol Conversion Malini Bhandaru, Bruce Draper and Victor Lesser, University of Massachusetts at Amherst Transformation-invariant Indexing and Machine Discovery for Computer Vision Darrell Conklin, Queen's University Recognition and Learning of Unknown Objects in a Hierarchical Knowledge-base L. Dey, P.P. Das, and S. Chaudhury, I.I.T., Delhi Unsupervised Learning of Object Models C. K. I. Williams, R. S. Zemel, Univ. of Toronto; M. C. Mozer, Univ. of Colorado 2:00 - 3:30-- moderated by Pat Langley, Siemens Corporate Research Learning and Recognition of 3-D Objects from Brightness Images Hiroshi Murase and Shree K. Nayar, Columbia University Adaptive Image Segmentation Using Multi-Objective Evaluation and Hybrid Search Methods Bir Bhanu, Sungkee Lee, Subhodev Das, University of California Learning 3D Object Recognition Models from 2D Images Arthur R. Pope and David G. Lowe, University of British Columbia Matching and Clustering: Two Steps Towards Automatic Objective Model Generation Patric Gros, LIFIA, Grenoble, France Learning About A Scene Using an Active Vision System P. Remagnino, M. Bober and J. Kittler, University of Surrey, UK 4:00 - 5:30-- moderated by Bruce Draper, University of Massachusetts Learning Indexing Functions for 3-D Model-Based Object Recognition Jeffrey S. Beis and David G. Lowe, University of British Columbia Non-accidental Features in Learning Richard Mann and Allan Jepson, University of Toronto Feature-Based Recognition of Objects Paul A. Viola, Massachusetts Institute of Technology Learning Correspondences Between Visual Features and Functional Features \\ Hitoshi Matsubara, Katsuhiko Sakaue and Kazuhiko Yamamoto, ETL, Japan 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 Saturday, October 23 9:00 - 10:30-- Exciting and Controversial Invited talks on Learning and Vision Machine Learning and Computer Vision: An odd couple that could be ideal Ramesh Jain, University of California at San Diego. Reinforcement Learning and Computer Vision Rich Sutton, GTE Research Labs 11:00 - 12:30-- moderated by Sridhar Mahadevan, University of South Florida Learning from the Schema Learning System Bruce Draper, University of Massachusetts Learning Symbolic Names for Perceived Colors J.M. Lammens and S.C. Shapiro, SUNY Buffalo Extracting a Domain Theory from Natural Language to Construct a Knowledge Base for Visual Recognition Lawrence Chachere and Thierry Pun, University of Geneva Symbolic and Subsymbolic Learning for Vision: Some Possibilities Vasant Honavar, Iowa State University A Vision-Based Learning Method for Pushing Manipulation, Marcos Salganicoff, Univ. of Pennsylvania; Giorgio Metta, Andrea Oddera and Giulio Sandini, University of Genoa. 2:00 - 3:30-- moderated by Randall Nelson, University of Rochester A Classifier System for Learning Spatial Representations Based on a Morphological Wave Propagation Algorithm Michael M. Skolnick, R.P.I. 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 Developing Population Codes for Object Instantiation Parameters Richard S. Zemel, Geoffrey E. Hinton, University of Toronto Integration of Machine Learning and Vision into an Active Agent Paradigm Peter W. Pachowicz, George Mason University Assembly plan from observation K. Ikeuchi and S.B. Kang, Carnegie-Mellon University 4:00 - 5:30-- moderated by Robin Murphy, Colorado School of Mines Learning Shape Models for a Vision Based Human-Computer Interface Jakub Segen, A.T.\&T. Bell Laboratories Learning Visual Speech G. J. Wolff, K. V. Prasad, D. G. Stork & M. Hennecke, Ricoh California Research Center Learning open loop control of complex motor tasks Jeff Schneider, University of Rochester Issues in Learning from Noisy Sensory Data J. Bala and P. Pachowicz, George Mason University Learning combination of evidence functions in object recognition D. Cook, L. Hall, L. Stark and K. Bowyer, University of South Florida Sunday, October 24 9:00 - 10:30-- moderated by Abraham Waksman, Air Force Office of Scientific Research Exciting and Controversial Panel Discussion: Managing resource boundedness and achieving scale-up with the help of machine learning 11:00 - 12:30-- moderated by Bir Bhanu, University of California at Riverside Learning for Vision: Up with the Gigabyte! Death to the Functional View! Randal Nelson, University of Rochester Learning to Eliminate Background Effects in Object Recognition Robin R. Murphy, Colorado School of Mines The Prax Approach to Learning a Large Number of Texture Concepts J. Bala, R. Michalski, and J. Wnek, George Mason University Non-Intrusive Gaze Tracking Using Artificial Neural Networks Dean A. Pomerleau and Shumeet Baluja, Carnegie Mellon University Toward a General Solution to the Symbol Grounding Problem: Combining Machine Learning and Computer Vision Paul Davidsson, Lund University