Title :
Least-squares congealing for large numbers of images
Author :
Cox, Mark ; Sridharan, Sridha ; Lucey, Simon ; Cohn, Jeffrey
Author_Institution :
Queensland Univ. of Technol., Brisbane, QLD, Australia
fDate :
Sept. 29 2009-Oct. 2 2009
Abstract :
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.
Keywords :
image matching; inverse problems; least squares approximations; Lucas & Kanade image alignment algorithm; inverse compositional strategy; least squares congealing; Additives; Australia; Convergence; Cost function; Employment; Entropy; Iterative algorithms; Object detection; Object recognition; Robots;
Conference_Titel :
Computer Vision, 2009 IEEE 12th International Conference on
Conference_Location :
Kyoto
Print_ISBN :
978-1-4244-4420-5
Electronic_ISBN :
1550-5499
DOI :
10.1109/ICCV.2009.5459430