Most cost function based clustering or partitioning methods measure the compactness of groups of data. In contrast to this picture of a point source in feature space, some data sou...
We use cluster analysis as a unifying principle for problems from low, middle and high level vision. The clustering problem is viewed as graph partitioning, where nodes represent ...
We present a method that automatically partitions a single image into non-overlapping regions coherent in texture and colour. An assumption that each textured or coloured region ca...
Many machine learning algorithms for clustering or dimensionality reduction take as input a cloud of points in Euclidean space, and construct a graph with the input data points as...
Abstract. Autonomous collision avoidance in vehicles requires an accurate seperation of obstacles from the background, particularly near the focus of expansion. In this paper, we p...
Andreas Wedel, Thomas Schoenemann, Thomas Brox, Da...