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Proceedings Paper

Statistical simulation of deformations using wavelet independent component analysis
Author(s): Ahmed Elsafi; Rami Zewail; Nelson Durdle
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Paper Abstract

Statistical models of deformations are becoming crucial tools for a variety of computer vision applications such as regularization and validation of image registration and segmentation algorithms. In this article, we propose a new approach to effectively represent the statistical properties of high dimensional deformations. In particular, we propose techniques that use independent component analysis (ICA) in conjunction with wavelet packet decomposition. Two different architectures for ICA have been investigated; one treats the elastic deformations as random variables and the individual deformation field as outcomes and a second which treats the individual deformations as random variables and the elastic deformations as outcomes. The experiments were conducted using the Amsterdam Library of Images (ALOI), and the proposed algorithms were evaluated using the model generalization as a statistical measure. Experimental results show a significant improvement when compared to a recent deformation representation in the literature.

Paper Details

Date Published: 1 April 2008
PDF: 8 pages
Proc. SPIE 6978, Visual Information Processing XVII, 697813 (1 April 2008); doi: 10.1117/12.785658
Show Author Affiliations
Ahmed Elsafi, Univ. of Alberta (Canada)
Rami Zewail, Univ. of Alberta (Canada)
Nelson Durdle, Univ. of Alberta (Canada)

Published in SPIE Proceedings Vol. 6978:
Visual Information Processing XVII
Zia-ur Rahman; Stephen E. Reichenbach; Mark Allen Neifeld, Editor(s)

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