We present a Kernighan-Lin style local improvement heuristic for genetic algorithms. We analyze the run-time cost of the heuristic. We demonstrate through experiments that the heur...
Higher order energy functions have the ability to encode
high level structural dependencies between pixels, which
have been shown to be extremely powerful for image labeling
pro...
Carsten Rother (Microsoft Research Cambridge), Pus...
In many applications of graphical models arising in computer vision, the hidden variables of interest are most naturally specified by continuous, non-Gaussian distributions. There...
Erik B. Sudderth, Alexander T. Ihler, William T. F...
The popular bag-of-features representation for object recognition collects signatures of local image patches and discards spatial information. Some have recently attempted to at l...
In this paper, we present a probabilistic framework for automatic detection and tracking of objects. We address the data association problem by formulating the visual tracking as ...