Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learni...
Carolina Galleguillos, Boris Babenko, Andrew Rabin...
Tracking multiple interacting objects represents a challenging area in computer vision. The tracking problem in general can be formulated as the task of recovering the spatio-temp...
Recently several researchers have investigated techniques for using data to learn Bayesian networks containing compact representations for the conditional probability distribution...
David Maxwell Chickering, David Heckerman, Christo...
The Hurst parameter H characterizes the degree of long-range dependence (and asymptotic selfsimilarity) in stationary time series. Many methods have been developed for the estimat...
Stilian Stoev, Murad S. Taqqu, Cheolwoo Park, Geor...
Object localization using sensed data features and corresponding model features is a fundamental problem in machine vision. We reformulate object localization as a least squares p...