Least-Squares Congealing for Large Numbers of Images

Mark Cox, Simon Lucey, Sridha Sridharan, and Jeffrey Cohn
International Conference on Computer Vision (ICCV), August, 2009.

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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.

Congealing, Unsupervised Image Ensemble Alignment

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Text Reference
Mark Cox, Simon Lucey, Sridha Sridharan, and Jeffrey Cohn, "Least-Squares Congealing for Large Numbers of Images," International Conference on Computer Vision (ICCV), August, 2009.

BibTeX Reference
   author = "Mark Cox and Simon Lucey and Sridha Sridharan and Jeffrey Cohn",
   title = "Least-Squares Congealing for Large Numbers of Images",
   booktitle = "International Conference on Computer Vision (ICCV)",
   month = "August",
   year = "2009",