Learning Shape Descriptions

We report on initial experiments with an implemented learning system whose inputs are images of two-dimensional shapes. The system first builds semantic network shape descriptions based on Brady's smoothed local symmetry representation. It learns shape models from them using a modified version of Winston's ANALOGY program. The learning program uses only positive examples, and is capable of learning disjunctive concepts. We discuss the lcarnability of shape descriptions.

[1]  Daniel G. Bobrow,et al.  On Overview of KRL, a Knowledge Representation Language , 1976, Cogn. Sci..

[2]  M. Brady,et al.  Smoothed Local Symmetries and Their Implementation , 1984 .

[3]  Patrick Henry Winston,et al.  Learning and reasoning by analogy , 1980, CACM.

[4]  Berthold K. P. Horn.  Understanding Image Intensities , 1977, Artif. Intell..

[5]  Hector J. Levesque,et al.  Krypton: A Functional Approach to Knowledge Representation , 1983, Computer.

[6]  P. Winston.  Learning by Augmenting Rules and Accumulating Censors. , 1982 .

[7]  Patrick Henry Winston,et al.  Learning structural descriptions from examples , 1970 .

[8]  J. Canny.  Finding Edges and Lines in Images , 1983 .

[9]  Margaret M. Fleck.  Local Rotational Symmetries , 1985 .

[10]  Ramakant Nevatia,et al.  Description and Recognition of Curved Objects , 1977, Artif. Intell..

[11]  Michael Brady,et al.  The Mechanic's Mate , 1984, ECAI.

[12]  D. Marr,et al.  Representation and recognition of the spatial organization of three-dimensional shapes , 1978, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[13]  M. Brady.  Criteria for Representations of Shape , 1983 .

[14]  Randall Davis,et al.  Diagnostic Reasoning Based on Structure and Behavior , 1984, Artif. Intell..

[15]  Patrick Henry Winston,et al.  Learning New Principles from Precedents and Exercises , 1982, Artif. Intell..

[16]  Rodney A. Brooks,et al.  Symbolic Reasoning Among 3-D Models and 2-D Images , 1981, Artif. Intell..

[17]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[18]  Noam Chomsky,et al.  The Sound Pattern of English , 1968 .

[19]  William A. Woods,et al.  What's in a Link: Foundations for Semantic Networks , 1975 .

[20]  Kurt VanLehn,et al.  Felicity conditions for human skill acquisition: validating an ai-based theory , 1983 .

[21]  D. D. Hoffman,et al.  Representing Smooth Plane Curves for Recognition: Implications for Figure-Ground Reversal , 1982, AAAI.

[22]  Michael R. Lowry,et al.  Learning Physical Descriptions From Functional Definitions, Examples, and Precedents , 1983, AAAI.

[23]  Dana H. Ballard,et al.  Strip trees: a hierarchical representation for curves , 1981, CACM.

[24]  W. A. Perkins,et al.  A Model-Based Vision System for Industrial Parts , 1978, IEEE Transactions on Computers.

[25]  Herbert Freeman,et al.  Computer Processing of Line-Drawing Images , 1974, CSUR.

[26]  Thomas G. Dietterich,et al.  Inductive Learning of Structural Descriptions: Evaluation Criteria and Comparative Review of Selected Methods , 1981, Artif. Intell..

[27]  D. Marr,et al.  Analysis of occluding contour , 1977, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[28]  HARRY BLUM,et al.  Shape description using weighted symmetric axis features , 1978, Pattern Recognit..

[29]  Ronald J. Brachman,et al.  What IS-A Is and Isn't: An Analysis of Taxonomic Links in Semantic Networks , 1983, Computer.