Image segmentation is conventionally formulated as a pixellabeling problem, in which “hard” decisions have to be made to partition pixels into regions. As image segmentation i...
In this paper we propose a new probability update rule and sampling procedure for population-based incremental learning. These proposed methods are based on the concept of opposit...
We study the problem of hierarchical classification when labels corresponding to partial and/or multiple paths in the underlying taxonomy are allowed. We introduce a new hierarchi...
This paper presents a scalable solution to the problem of tracking objects across spatially separated, uncalibrated cameras with non overlapping fields of view. The approach relie...
We give a fast and practical algorithm for statistical learning hyperparameters from observable data in probabilistic image processing, which is based on Gaussian graphical model ...