Example Based Non-rigid Shape Detection

  • Yefeng Zheng
  • Xiang Sean Zhou
  • Bogdan Georgescu
  • Shaohua Kevin Zhou
  • Dorin Comaniciu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3954)


Since it is hard to handcraft the prior knowledge in a shape detection framework, machine learning methods are preferred to exploit the expert annotation of the target shape in a database. In the previous approaches [1,2] , an optimal similarity transformation is exhaustively searched for to maximize the response of a trained classification model. At best, these approaches only give a rough estimate of the position of a non-rigid shape. In this paper, we propose a novel machine learning based approach to achieve a refined shape detection result. We train a model that has the largest response on a reference shape and a smaller response on other shapes. During shape detection, we search for an optimal non-rigid deformation to maximize the response of the trained model on the deformed image block. Since exhaustive searching is inapplicable for a non-rigid deformation space with a high dimension, currently, example based searching is used instead. Experiments on two applications, left ventricle endocardial border detection and facial feature detection, demonstrate the robustness of our approach. It outperforms the well-known ASM and AAM approaches on challenging samples.


Convolutional Neural Network Shape Space Active Contour Model Weak Learner Target Shape 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yefeng Zheng
    • 1
  • Xiang Sean Zhou
    • 2
  • Bogdan Georgescu
    • 1
  • Shaohua Kevin Zhou
    • 1
  • Dorin Comaniciu
    • 1
  1. 1.Siemens Corporate ResearchPrincetonUSA
  2. 2.Siemens Medical SolutionsMalvernUSA

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