Comparative Analysis of Kernel Methods for Statistical Shape Learning

  • Yogesh Rathi
  • Samuel Dambreville
  • Allen Tannenbaum
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4241)


Prior knowledge about shape may be quite important for image segmentation. In particular, a number of different methods have been proposed to compute the statistics on a set of training shapes, which are then used for a given image segmentation task to provide the shape prior. In this work, we perform a comparative analysis of shape learning techniques such as linear PCA, kernel PCA, locally linear embedding and propose a new method, kernelized locally linear embedding for doing shape analysis. The surfaces are represented as the zero level set of a signed distance function and shape learning is performed on the embeddings of these shapes. We carry out some experiments to see how well each of these methods can represent a shape, given the training set.


Feature Space Kernel Method Locally Linear Embedding Signed Distance Function Kernel Space 
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

  • Yogesh Rathi
    • 1
  • Samuel Dambreville
    • 1
  • Allen Tannenbaum
    • 1
  1. 1.Georgia Institute of TechnologyAtlantaUSA

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