Curve segmentation using directional information, relation to pattern detection

Abstract

©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.Presented at the 2005 International Conference on Image Processing (ICIP)September 11-14, 2005, Genova, Italy.DOI: 10.1109/ICIP.2005.1530175We propose an extension of the conformal (or geodesic) active contour framework in which the conformal factor depends not only on the position of the curve but also on the direction of its tangent. We describe several properties for variational curve segmentation schemes that justify the construction of optimal conformal factors (i.e., learning) in strong connection with pattern matching. The determination of optimal curves (i.e., segmentation) can be performed using either the calculus of variations or dynamic programming. The technique is illustrated on a road detection problem for different signal to noise ratios