An efficient stochastic approach to groupwise non-rigid image registration

Sidorov, Kirill A. ORCID: https://orcid.org/0000-0001-7935-4132, Richmond, Stephen ORCID: https://orcid.org/0000-0001-5449-5318 and Marshall, D. 2009. An efficient stochastic approach to groupwise non-rigid image registration. IEEE. 10.1109/CVPRW.2009.5206516

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Abstract

The groupwise approach to non-rigid image registration, solving the dense correspondence problem, has recently been shown to be a useful tool in many applications, in- cluding medical imaging, automatic construction of statis- tical models of appearance and analysis of facial dynam- ics. Such an approach overcomes limitations of traditional pairwise methods but at a cost of having to search for the solution (optimal registration) in a space of much higher dimensionality which grows rapidly with the number of ex- amples (images) being registered. Techniques to overcome this dimensionality problem have not been addressed suffi- ciently in the groupwise registration literature. In this paper, we propose a novel, fast and reliable, fully unsupervised stochastic algorithm to search for optimal groupwise dense correspondence in large sets of unmarked images. The efficiency of our approach stems from novel di- mensionality reduction techniques specific to the problem of groupwise image registration and from comparative insen- sitivity of the adopted optimisation scheme (Simultaneous Perturbation Stochastic Approximation (SPSA)) to the high dimensionality of the search space. Additionally, our algo- rithm is formulated in way readily suited to implementation on graphics processing units (GPU). In evaluation of our method we show a high robust- ness and success rate, fast convergence on various types of test data, including facial images featuring large degrees of both inter- and intra-person variation, and show consid- erable improvement in terms of accuracy of solution and speed compared to traditional methods.

Item Type:	Book
Book Type:	Authored Book
Date Type:	Publication
Status:	Published
Schools:	Dentistry
Publisher:	IEEE
Last Modified:	18 Oct 2022 13:58
URI:	https://orca.cardiff.ac.uk/id/eprint/16061

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