Motion-based segmentation of image sequences

Abstract: This Master's Thesis addresses the problem of segmenting an image sequence with respect to the motion in the sequence. As a basis for the motion estimation, 3D orientation tensors are used. The goal of the segmentation is to partition the images into regions, characterized by having a coherent motion. The motion model is affine with respect to the image coordinates. A method to estimate the parameters of the motion model from the orientation tensors in a region is presented. This method can also be generalized to a large class of motion models. Two segmentation algorithms are presented together with a postprocessing algorithm. All these algorithms are based on the competitive algorithm, a general method for distributing points between a number of regions, without relying on arbitrary threshold values. The first segmentation algorithm segments each image independently, while the second algorithm recursively takes advantage of the previous segmentation. The postprocessing algorithm stabilizes the segmentations of a whole sequence by imposing continuity constraints. The algorithms have been implemented and the results of applying them to a test sequence are presented. Interesting properties of the algorithms are that they are robust to the aperture problem and that they do not require a dense velocity ¯eld. It is finally discussed how the algorithms can be developed and improved. It is straightforward to extend the algorithms to base the segmentations on alternative or additional features, under not too restrictive conditions on the features.