Using Markov Random Fields for Contour-Based Grouping

or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the IEEE. Contact: IEEE Intellectual Property Rights Office / IEEE Service Center / 445 Hoes Lane / Piscataway, NJ 08855-1331 / phone: (732) 562-3966 / fax: (732) 981-8062 In Proceedings International Conference on Image Processing, volume II, pages 207-210. IEEE, 1997. Int. Conference on Image Processing, Santa Barbara, 1997, Vol II, pp 207-210 Copyright 1997 IEEE. Published in the 1997 International Conference on Image Processing (ICIP'97), scheduled for October 26-29, 1997 in Santa Barbara, CA. 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 redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the IEEE. Contact: Manager, Copyrights and Permissions / IEEE Service Center / 445 Hoes Lane / P.O. Box 1331 / Piscataway, NJ 08855-1331, USA. Telephone: + Intl. 908-562-3966. Using Markov Random Fields for Contour-Based Grouping Anke Ma mann, Stefan Posch, Gerhard Sagerer, Daniel Schl uter AG Angewandte Informatik, Universitat Bielefeld P.O. Box 100131, 33501 Bielefeld, Germany e-mail: [email protected] Abstract To overcome fragmentation of an initial contourbased segmentation and to organize contour segments into image primitives on a higher level of abstraction, regularities of the image data are exploited using ideas from the Gestalt psychology. First, groups are hypothesized within a hierarchy based on local evidence only, where the criteria are derived from a hand labelled training set. These hypotheses are subsequently judged in a global context using a Markov Random Field to derive a global interpretation. Examples of results for real data are given.