Department of Computer Science
Bidirectional deformable matching with application to handwritten character extraction
To achieve integrated segmentation and recognition in complex scenes, the model-based approach has widely been accepted as a promising paradigm. However, the performance is still far from satisfactory when the target object is highly deformed and the level of outlier contamination is high. In this paper, we first describe two Bayesian frameworks, one for classifying input patterns and another for detecting target patterns in complex scenes using deformable models. Then, we show that the two frameworks are similar to the forward-reverse setting of Hausdorff matching and that their matching and discriminating properties are complementary to each other. By properly combining the two frameworks, we propose a new matching scheme called bidirectional matching. This combined approach inherits the advantages of the two Bayesian frameworks. In particular, we have obtained encouraging empirical results on shape-based pattern extraction, using a subset of the CEDAR handwriting database containing handwritten words of highly varying shape.
Deformable models, Shape, Stereo vision, Layout, Application software, Bayesian methods, Computer vision, Data mining, Contamination, Image segmentation
Source Publication Title
IEEE Transactions on Pattern Analysis and Machine Intelligence
Institute of Electrical and Electronics Engineers (IEEE)
The authors would like to thank the Hong Kong Research Grants Council (RGC) for supporting this research through two research grants (RGC 746/96E and RGC 6081/97E).
Link to Publisher's Edition
Cheung, Kwok-Wai, Dit-Yan Yeung, and Roland T. Chin. "Bidirectional deformable matching with application to handwritten character extraction." IEEE Transactions on Pattern Analysis and Machine Intelligence 24.8 (2002): 1133-1139.