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Distribution Metrics and Image Segmentation
Tryphon Georgiou1, Oleg Michailovich, Yogesh Rathi
1Tryphon Georgiou is with the Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, 55455 (email: georgiou@ece.umn.edu ). O. Michailovich was with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA. He is currently with the Department of Electrical and Computer Engineering, University of Alberta, Canada T6G 2E1 (e-mail: olegm@ece.ualberta.ca ). Y. Rathi, James Malcolm, A. Tannenbaum are with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA (e-mail: yogesh.rathi@gatech.edu ; malcolm@ece.gatech.edu ; tannenba@ece.gatech.edu ). Tannenbaum is also with the Department of Electrical Engineering, Technion, Israel where he is supported by a Marie Curie Grant through the EU.
Abstract:
The purpose of this paper is to describe certain alternative metrics for quantifying distances between distributions, and to explain their use and relevance in visual tracking. Besides the theoretical interest, such metrics may be used to design filters for image segmentation, that is for solving the key visual task of separating an object from the background in an image. The segmenting curve is represented as the zero level set of a signed distance function. Most existing methods in the geometric active contour framework perform segmentation by maximizing the separation of intensity moments between the interior and the exterior of an evolving contour. Here one can use the given distributional metric to determine a flow which minimizes changes in the distribution inside and outside the curve.
