Given discrete event data, we wish to produce a probability density that can model the relative probability of events occurring in a spatial region. Common methods of density esti...
Laura M. Smith, Matthew S. Keegan, Todd Wittman, G...
Recent work on background subtraction has shown developments on two major fronts. In one, there has been increasing sophistication of probabilistic models, from mixtures of Gaussi...
Manjunath Narayana, Allen R. Hanson, Erik G. Learn...
We propose an information-theoretic method for multi-phase image segmentation, in an active contour-based framework. Our approach is based on nonparametric density estimates, and ...
Alan S. Willsky, Anthony J. Yezzi, John W. Fisher ...
We propose a kernel-density based scheme that incorporates the object colors with their spatial relevance to track the object in a video sequence. The object is modeled by the col...
Naive Bayes classifier is a frequently used method in various natural language processing tasks. Inspired by a modified version of the method called the flexible Bayes classifier, ...
Tapio Pahikkala, Jorma Boberg, Aleksandr Myllä...