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Proceedings Paper

Steerable optical flow based image registration: application to aligning human torso images
Author(s): Ahmed Elsafi; Rami Zewail; Nelson Durdle
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Paper Abstract

The aim of image registration is to align two or more images taken from different viewpoints, at different time instances, or by different modalities. Image registration methods are divided into two main categories, feature based and intensity based methods. Recently intensity based methods have gained popularity since they aim at finding a dense correspondence between the images needed to be aligned without calculating correspondence between salient features. In this work, a new intensity based image registration method has been proposed and tested. This method models the source and target image as a single image displaced over time and calculates the optical flow fields in a multiresolution framework. In order to have the ability to represent complex fields, the deformation has been modelled as locally affine but globally smooth. Multiresolution image representation by steerable pyramid decomposition is integrated with the differential image registration technique in order to find accurate image deformations. The usage of steerable pyramid overcomes traditional problems in other pyramidal methods namely aliasing across different bands, lack of translation and rotation invariance. The new algorithm was validated using torso images for volunteers at the University of Alberta in addition to images captured of a cast model of the human torso. Experiments have demonstrated promising results in terms of root mean square error and average pixel error.

Paper Details

Date Published: 27 April 2009
PDF: 7 pages
Proc. SPIE 7341, Visual Information Processing XVIII, 73410F (27 April 2009); doi: 10.1117/12.819382
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. 7341:
Visual Information Processing XVIII
Zia-Ur Rahman; Stephen E. Reichenbach; Mark Allen Neifeld, Editor(s)

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