Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

On deformable models for visual pattern recognition

Language

English

Abstract

This paper reviews model-based methods for non-rigid shape recognition. These methods model, match and classify non-rigid shapes, which are generally problematic for conventational algorithms using rigid models. Issues including model representation, optimization criteria formulation, model matching, and classification are examined in detail with the objective to provide interested researchers a roadmap for exploring the field. This paper emphasizes on 2D deformable models. Their potential applications and future research directions, particularly on deformable pattern classification, are discussed.

Keywords

Deformable models, Model representation, Criteria formulation, Matching, Classification, Topology adaptation, Regularization, Optimization, Initialization, Constraint incorporation

Publication Date

7-2002

Source Publication Title

Pattern Recognition

Volume

35

Start Page

1507

End Page

1526

Publisher

Elsevier

Peer Reviewed

1

Funder

This research work was supported in part by the Hong Kong Research Grants Council (RGC) under Competitive Earmarked Research Grants HKUST 746/96E and HKUST 6081/97E.

DOI

10.1016/S0031-3203(01)00135-2

Link to Publisher's Edition

http://dx.doi.org/10.1016/S0031-3203(01)00135-2

ISSN (print)

00313203

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