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T. {Bischof, W. F. & Caelli}
ABSTRACT
We discuss automatic rule generation techniques for learning relational properties of 2-D visual patterns and 3-D objects from training samples where the observed feature values are continuous. In particular, we explore a conditional rule generation method that defines patterns (or objects) in terms of ordered lists of bounds on unary (pattern part) and binary (part relation) features. The technique, termed Conditional Rule Generation (CRG), was developed to integrate relational structure representations of patterns and the generalization characteristics of evidenced-based systems (EBS). We show how this technique can be used for recognition of complex patterns and of objects in scenes. Further, we show the extent to which the learned rules can identify patterns and objects that have undergone non-rigid distortions. 
ECVision indexed and annotated bibliography of cognitive computer vision publications
This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1
The complete text version of this BibTeX file is available here: ECVision_bibliography.bib
Visual Learning of Patterns and ObjectsSite generated on Friday, 06 January 2006