Accession Number:

ADA463031

Title:

Comparative Analysis of Kernel Methods for Statistical Shape Learning

Descriptive Note:

Conference paper

Corporate Author:

GEORGIA INST OF TECH ATLANTA BIOENGINEERING CENTER

Report Date:

2006-01-01

Pagination or Media Count:

13.0

Abstract:

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.

Subject Categories:

  • Cybernetics
  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE