Title
Shape Registration By Simultaneously Optimizing Representation And Transformation
Abstract
This paper proposes a novel approach that achieves shape registration by optimizing shape representation and transformation simultaneously, which are modeled by a constrained Gaussian Mixture Model (GMM) and a regularized thin plate spline respectively. The problem is formulated within a Bayesian framework and solved by an expectation-maximum (EM) algorithm. Compared with the popular methods based on landmarks-sliding, its advantages include: (1) It can naturally deal with shapes of complex topologies and 3D dimension; (2) It is more robust against data noise; (3) The registration performance is better in terms of the generalization error of the resultant statistical shape model. These are demonstrated on both synthetic and biomedical shapes. © Springer-Verlag Berlin Heidelberg 2007.
Publication Date
1-1-2007
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
4792 LNCS
Issue
PART 2
Number of Pages
809-817
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1007/978-3-540-75759-7_98
Copyright Status
Unknown
Socpus ID
84883836472 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84883836472
STARS Citation
Jiang, Yifeng; Xie, Jun; Sun, Deqing; and Tsui, Hungtat, "Shape Registration By Simultaneously Optimizing Representation And Transformation" (2007). Scopus Export 2000s. 7219.
https://stars.library.ucf.edu/scopus2000/7219