Abstract
An extension to the popular fuzzy c-means clustering method is proposed by introducing an additional disparity cue. The creation of the fuzzy clusters is driven by a degree of the stereo match and thus it enables to separate the objects not only by their different colours but also on their different spatial depth. In contrast to the other approaches, the clustering is not performed on the individual input images, but on the stereo image pairs and takes into accounts the stereo matching properties known from the stereo matching algorithms. The proposed method is capable of calculating the output segmentations, as well as the disparity maps. The results of the algorithm show that the proposed method can improve the segmentation in difficult settings. However, the drawback of this approach is that it requires the stereo image pairs of the segmented scenes that are not always easily obtainable.
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