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.
Bibliographical noteFunding Information:
This work was supported in part by Grants from NSF, AFOSR, ARO, MURI, MRI-HEL as well as by a Grant from NIH (NAC P41 RR-13218) through Brigham and Women’s Hospital. This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on the National Centers for Biomedical Computing can be obtained from http://nihroadmap.nih.gov/bioinformatics. ∗ Corresponding author. E-mail addresses: firstname.lastname@example.org (T. Georgiou), email@example.com (O. Michailovich), yogesh. firstname.lastname@example.org (Y. Rathi), email@example.com (J. Malcolm), firstname.lastname@example.org (A. Tannenbaum). 1 Present address: Department of Electrical and Computer Engineering, University of Alberta, Canada T6G 2E1. 2 He is also with the Department of Electrical Engineering, Technion, Israel where he is supported by a Marie Curie Grant through the EU.
- Distance measures