Least-squares congealing for large numbers of images

Cox, Mark, Sridharan, Sridha, Lucey, Simon, & Cohn, Jeffrey (2009) Least-squares congealing for large numbers of images. In 2009 IEEE 12th International Conference on Computer Vision. IEEE Computer Society, pp. 1949-1956.

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In this paper we pursue the task of aligning an ensemble of images in an unsupervised manner. This task has been commonly referred to as “congealing” in literature. A form of congealing, using a least-squares criteria, has been recently demonstrated to have desirable properties over conventional congealing. Least-squares congealing can be viewed as an extension of the Lucas & Kanade (LK)image alignment algorithm. It is well understood that the alignment performance for the LK algorithm, when aligning a single image with another, is theoretically and empirically equivalent for additive and compositional warps. In this paper we: (i) demonstrate that this equivalence does not hold for the extended case of congealing, (ii) characterize the inherent drawbacks associated with least-squares congealing when dealing with large numbers of images, and (iii) propose a novel method for circumventing these limitations through the application of an inverse-compositional strategy that maintains the attractive properties of the original method while being able to handle very large numbers of images.

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7 citations in Web of Science®

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ID Code: 46295
Item Type: Book Chapter
DOI: 10.1109/ICCV.2009.5459430
ISBN: 97814244441909
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
Deposited On: 03 Oct 2011 05:11
Last Modified: 17 Oct 2015 16:16

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