A level set framework using a new incremental, robust Active Shape Model for object segmentation and tracking

Authors:
Michael Fussenegger;Peter Roth;Horst Bischof;Rachid Deriche;Axel Pinz
Affiliations:
Institute of Electrical Measurement and Measurement Signal Processing, Graz University of Technology, Kopernikusgasse 24, A-8010 Graz, Austria;Institute for Computer Graphics and Vision, Graz University of Technology, Austria;Institute for Computer Graphics and Vision, Graz University of Technology, Austria;Odyssée Project Team INRIA Sophia Antipolis - Méditerranée, France;Institute of Electrical Measurement and Measurement Signal Processing, Graz University of Technology, Kopernikusgasse 24, A-8010 Graz, Austria
Venue:
Image and Vision Computing
Year:
2009

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Abstract

Level set based approaches are widely used for image segmentation and object tracking. As these methods are usually driven by low level cues such as intensity, colour, texture, and motion they are not sufficient for many problems. To improve the segmentation and tracking results, shape priors were introduced into level set based approaches. Shape priors are generated by presenting many views a priori, but in many applications this a priori information is not available. In this paper, we present a level set based segmentation and tracking method that builds the shape model incrementally from new aspects obtained by segmentation or tracking. In addition, in order to tolerate errors during the segmentation process, we present a robust Active Shape Model, which provides a robust shape prior in each level set iteration step. For the tracking, we use a simple decision function to maintain the desired topology for multiple regions. We can even handle full occlusions and objects, which are temporarily hidden in containers by combining the decision function and our shape model. Our experiments demonstrate the improvement of the level set based segmentation and tracking using an Active Shape Model and the advantages of our incremental, robust method over standard approaches.