Many vision tasks can be formulated as partitioning an adjacency graph through optimizing a Bayesian posterior probability p defined on the partition-space. In this paper two appr...
Abstract. Bayesian inference provides a powerful framework to optimally integrate statistically learned prior knowledge into numerous computer vision algorithms. While the Bayesian...
We propose a new family of non-submodular global energy functions that still use submodularity internally to couple edges in a graph cut. We show it is possible to develop an efï¬...
In this work, we present a common framework for seeded image segmentation algorithms that yields two of the leading methods as special cases - The Graph Cuts and the Random Walker...
In this paper, we propose a modification to the Boykov-Kolmogorov maximum flow algorithm [2] in order to make the algorithm preserve the topology of an initial interface. This alg...