TY - GEN
T1 - Non-rigid image registration using geometric features and local salient region features
AU - Yang, Jinzhong
AU - Williams, James P.
AU - Sun, Yiyong
AU - Blum, Rick S.
AU - Xu, Chenyang
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2006
Y1 - 2006
N2 - 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 Deformahle 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.
AB - 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 Deformahle 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.
UR - http://www.scopus.com/inward/record.url?scp=33845578203&partnerID=8YFLogxK
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U2 - 10.1109/CVPR.2006.208
DO - 10.1109/CVPR.2006.208
M3 - Conference contribution
AN - SCOPUS:33845578203
SN - 0769525970
SN - 9780769525976
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 825
EP - 832
BT - Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
T2 - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
Y2 - 17 June 2006 through 22 June 2006
ER -