NDLI: Groupwise point pattern registration using a novel CDF-based Jensen-Shannon Divergence

Content Provider	IEEE Xplore Digital Library
Author	Fei Wang Vemuri, B.C. Rangarajan, A.
Copyright Year	2006
Description	Author affiliation: University of Florida, Gainesville, FL, 32611 USA (Fei Wang)
Abstract	In this paper, we propose a novel and robust algorithm for the groupwise non-rigid registration of multiple unlabeled point-sets with no bias toward any of the given pointsets. To quantify the divergence between multiple probability distributions each estimated from the given point sets, we develop a novel measure based on their cumulative distribution functions that we dub the CDF-JS divergence. The measure parallels the well known Jensen-Shannon divergence (defined for probability density functions) but is more regular than the JS divergence since its definition is based on CDFs as opposed to density functions. As a consequence, CDF-JS is more immune to noise and statistically more robust than the JS. We derive the analytic gradient of the CDF-JS divergence with respect to the non-rigid registration parameters for use in the numerical optimization of the groupwise registration leading a computationally efficient and accurate algorithm. The CDF-JS is symmetric and has no bias toward any of the given point-sets, since there is NO fixed reference data set. Instead, the groupwise registration takes place between the input data sets and an evolving target dubbed the pooled model. This target evolves to a fully registered pooled data set when the CDF-JS defined over this pooled data is minimized. Our algorithm is especially useful for creating atlases of various shapes (represented as point distribution models) as well as for simultaneously registering 3D range data sets without establishing any correspondence. We present experimental results on non-rigid registration of 2D/3D real point set data.
Starting Page	1283
Ending Page	1288
File Size	593200
Page Count	6
File Format	PDF
ISBN	0769525970
ISSN	10636919
DOI	10.1109/CVPR.2006.131
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pattern matching Robustness Shape Computer vision Iterative closest point algorithm Information science Probability distribution Distribution functions Density measurement Probability density function
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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