Bidirectional deformable matching with application to handwritten character extraction

Kwok Wai CHEUNG*, Dit Yan Yeung, Roland T. CHIN

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1133-1139
Number of pages7
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume24
Issue number8
DOIs
Publication statusPublished - Aug 2002

Scopus Subject Areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

User-Defined Keywords

  • Bayesian inference
  • Bidirectional matching
  • Deformable models
  • Hausdorff matching
  • Model-based segmentation

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