Nonlinear Shape Prior from Kernel Space for Geometric Active Contours

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Title: Nonlinear Shape Prior from Kernel Space for Geometric Active Contours
Author: Dambreville, Samuel ; Rathi, Yogesh ; Tannenbaum, Allen R.
Abstract: The Geometric Active Contour (GAC) framework, which utilizes image information, has proven to be quite valuable for performing segmentation. However, the use of image information alone often leads to poor segmentation results in the presence of noise, clutter or occlusion. The introduction of shapes priors in the contour evolution proved to be an effective way to circumvent this issue. Recently, an algorithm was proposed, in which linear PCA (principal component analysis) was performed on training sets of data and the shape statistics thus obtained were used in the segmentation process. This approach was shown to convincingly capture small variations in the shape of an object. However, linear PCA assumes that the distribution underlying the variation in shapes is Gaussian. This assumption can be over-simplifying when shapes undergo complex variations. In the present work, we derive the steps for using Kernel PCA to in the GAC framework to introduce prior shape knowledge. Several experiments were performed using different training-sets of shapes. Starting with any initial contour, we show that the contour evolves to adopt a shape that is faithful to the elements of the training set. The proposed shape prior method leads to better performances than the one involving linear PCA.
Description: ©2006 SPIE--The International Society for Optical Engineering. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. The electronic version of this article is the complete one and can be found online at: http://dx.doi.org/10.1117/12.641708 DOI:10.1117/12.641708 Presented at Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 16-18 January 2006, San Jose, California, USA.
Type: Proceedings
URI: http://hdl.handle.net/1853/28809
ISSN: 0277-786X
Citation: Samuel Dambreville, Yogesh Rathi and Allen Tannenbaum, "Nonlinear Shape Prior from Kernel Space for Geometric Active Contours," Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, E.R. Dougherty, J.T. Astola, K.O. Egiazarian, N.M. Nasrabadi, S.A. Rizvi, Editors, Proc. of SPIE Vol. 6064, 606419 (2006)
Date: 2006-01
Contributor: Georgia Institute of Technology. Dept. of Biomedical Engineering
Emory University. Dept. of Biomedical Engineering
Georgia Institute of Technology. School of Electrical and Computer Engineering
Publisher: Georgia Institute of Technology
Society of Photo-Optical Instrumentation Engineers
Subject: Shape priors
Active contours
Principal component analysis
Kernel methods

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