Original scientific paper

Recognition and Learning with Polymorphic Structural Components

Mark Burge ; Johannes Kepler University, Dept. of Systems Science, Computer Vision Laboratory, Linz, Austria
Wilhelm Burger ; Johannes Kepler University, Dept. of Systems Science, Computer Vision Laboratory, Linz, Austria
Wolfgang Mayr ; Johannes Kepler University, Dept. of Systems Science, Computer Vision Laboratory, Linz, Austria

Abstract

We address the problem of describing, recognizing, and learning generic, free-form objects in real-world scenes. For this purpose, we have developed a hybrid appearance-based approach where objects are encoded as loose collections of parts and relations between neighboring parts. The key features of this approach are: part decomposition based on local structure segmentation derived from multi-scale wavelet filters, flexible and efficient recognition by combining weak structural constraints, and learning and generalization of generic object categories (with possibly large intra-class variability) from real examples.

Keywords

recognition; learning; polymorphic structural components; Gabor probe structural description

Hrčak ID:

150305

URI

https://hrcak.srce.hr/150305

Publication date:

30.3.1996.

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