Department of Computer Science
On deformable models for visual pattern recognition
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.
Deformable models, Model representation, Criteria formulation, Matching, Classification, Topology adaptation, Regularization, Optimization, Initialization, Constraint incorporation
Source Publication Title
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.
Link to Publisher's Edition
Cheung, K., Yeung, D., & Chin, R. (2002). On deformable models for visual pattern recognition. Pattern Recognition, 35, 1507-1526. https://doi.org/10.1016/S0031-3203(01)00135-2