By Topic

Non-rigid Image Registration Using Geometric Features and Local Salient Region Features

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Yang, J. ; Lehigh University, Bethlehem, PA 18015, USA ; Blum, R.S. ; Williams, J.P. ; Yiyong Sun
more authors

We present a novel feature-based non-rigid image registration algorithm using a small number of automatically extracted points and their associated local salient region features. Our automatic registration is a hybrid approach co-optimizing point-based and image-based terms. Motivated by the paradigm of the TPS-RPM algorithm [6], we develop the RHDM (Robust Hybrid Deformable Matching) algorithm by alternatively optimizing correspondences and transformations for registration. The local salient region features and the geometric features, together with the softassign and deterministic annealing techniques, are used for solving correspondences. Thin-plate splines are used for generating a smooth non-rigid spatial transformation. Our algorithm is built to be extremely robust to feature extraction errors. A new dynamic outlier rejection mechanism is described for rejecting outliers and generating accurate spatial mappings. A local refinement technique is used for correcting non-exactly matched correspondences arising from image noise and irregular deformations. In contrast with the TPS-RPM algorithm, which can handle only outliers in one point set, our algorithm is able to handle a considerable number of outliers in both point sets. The experimental results demonstrate the robustness and accuracy of our algorithm.

Published in:

Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on  (Volume:1 )

Date of Conference:

17-22 June 2006