Comparative analysis of kernel methods for statistical shape learning

Y. Rathi, S. Dambreville, A. Tannenbaum
Proceedings of the 2nd International Workshop on Computer Vision Approaches to Medical Image Analysis
Pages 96-107
2006

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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

Reference

Rathi Y, Dambreville S, Tannenbaum A. Comparative analysis of kernel methods for statistical shape learning. In Proceedings of the 2nd International Workshop on Computer Vision Approaches to Medical Image Analysis. CVAMIA, 2006;96-107.

Research areas

segmentation, shapeanalysis
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