Expectation Conditional Maximization-Based Deformable Shape Registration

Zheng, Guoyan (2013). Expectation Conditional Maximization-Based Deformable Shape Registration. Lecture notes in computer science, 8047, pp. 548-555. Berlin: Springer 10.1007/978-3-642-40261-6_66

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This paper addresses the issue of matching statistical and non-rigid shapes, and introduces an Expectation Conditional Maximization-based deformable shape registration (ECM-DSR) algorithm. Similar to previous works, we cast the statistical and non-rigid shape registration problem into a missing data framework and handle the unknown correspondences with Gaussian Mixture Models (GMM). The registration problem is then solved by fitting the GMM centroids to the data. But unlike previous works where equal isotropic covariances are used, our new algorithm uses heteroscedastic covariances whose values are iteratively estimated from the data. A previously introduced virtual observation concept is adopted here to simplify the estimation of the registration parameters. Based on this concept, we derive closed-form solutions to estimate parameters for statistical or non-rigid shape registrations in each iteration. Our experiments conducted on synthesized and real data demonstrate that the ECM-DSR algorithm has various advantages over existing algorithms.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB [discontinued]

UniBE Contributor:

Zheng, Guoyan

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health

ISSN:

0302-9743

ISBN:

978-3-642-40260-9

Publisher:

Springer

Language:

English

Submitter:

Guoyan Zheng

Date Deposited:

06 Jun 2014 16:50

Last Modified:

06 Jun 2014 16:50

Publisher DOI:

10.1007/978-3-642-40261-6_66

URI:

https://boris.unibe.ch/id/eprint/46493

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