Large margin learning of Continuous Density HMMs with a partially labeled dataset has been extensively studied in the speech and handwriting recognition fields. Yet due to the non...
We present an efficient method for maximizing energy functions with first and second order potentials, suitable for MAP labeling estimation problems that arise in undirected graph...
We propose a method that rates the suitability of given templates for template-based tracking in real-time. This is important for applications with online template selection, such...
In this paper we develop a probabilistic interpretation and a full Bayesian inference for non-negative matrix deconvolution (NMFD) model. Our ultimate goal is unsupervised extract...
Abstract— To advance robotic science it is important to perform experiments that can be replicated by other researchers to compare different methods. However, these comparisons t...