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We propose a general unsupervised multiscale approach towards image segmentation. The novelty of our method is based on the following points: firstly, it is general in the sense of being independent of the feature extraction process; secondly, it is unsupervised in that the number of classes is not assumed to be known a priori; thirdly, it is flexible as the decomposition sensitivity can be robustly...
In this paper we introduce a non-parametric clustering algorithm for 1-dimensional data. The procedure looks for the simplest (i.e. smoothest) density that is still compatible with the data. Compatibility is given a precise meaning in terms of the Kolmogorov-Smirnov statistic. After discussing experimental results for colour segmentation, we outline how this proposed algorithm can be extended to higher...
We describe a method for computing the likelihood that a completion joining two contour fragments passes through any given position and orientation in the image plane, that is, a method for completing the boundaries of partially occluded objects. Like computations in primary visual cortex (and unlike all previous models of contour completion in the human visual system), our computation is Euclidean...
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