Optimize Parameters Prune LabelsOptimize Configuration Motion Edges Color Depth

This paper presents an approach to multi-object image segmentation based on object motion using Markov Random Fields. To support the information gained from motion and to achieve robustness, several additional visual cues extracted from the image data are integrated. Depth information gained from stereo disparity is included to maintain segmentation in case an segmented object stops moving. Motion is estimated with a correspondence matching scheme. The approach differs from regular optical flow in the way that rich matching results are used for segmentation rather than only the best matches. The representation of segmented regions is realized implicitly as labeling on a 2D lattice. Motion segmentation is a key to many modern image processing applications. In video compression algorithms, the analysis of motion and regions with coherent motion helps to drastically reduce the amount of information that has to be stored and transmitted for each frame [11]. Motion segmentation and motion understanding, for example, plays an essential role in detecting and/or avoiding obstacles in vehicles or with a mobile robot. The rest of this paper is organized as follows: while Sect. 2 provides a short overview of the work done in the field of image segmentation and relations to our approach, Sect. 3 describes the architecture of the proposed system. Its evaluation is presented in Sect. 4. Finally, the paper concludes with Sect. 5.