Shape context for image understanding

Authors:
Lei He;Huazhou Liu
Affiliations:
Information Technology Department, Armstrong Atlantic State University, Savannah, GA;Electrical & Computer Engineering and Computer Science Department, University of Cincinnati, Cincinnati, OH
Venue:
SSIP'05 Proceedings of the 5th WSEAS international conference on Signal, speech and image processing
Year:
2005

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Abstract

This paper presents a robust object recognition and recovery method for image understanding using a recent shape feature descriptor: shape context. The novel feature is to unify both object recognition and recovery components into an image understanding system architecture, in which a complementary feedback structure can be incorporated to alleviate processing difficulties of each component alone. The idea is firstly to recognize the preliminary extracted object from a set of models by matching their shape contexts, then to apply the a priori shape information of the identified model for accurate object recovery. The output of the system is the recognized and segmented object. The shape matching method is illustrated by recognizing a set of CAPTCHA and animal silhouette examples with the presence of object translation and scaling, shape deformations and noise. Experiments of object recovery using real biomedical image samples, such as MR knee, have shown satisfactory results.