We present a probabilistic framework for recognizing objects in images of cluttered scenes. Hundreds of objects may be considered and searched in parallel. Each object is learned f...
We propose a solution to the problem of object recognition given a continuous video sequence containing multiple views of an object. Initially, object models are acquired from ima...
Traditional binary hypothesis testing relies on the precise knowledge of the probability density of an observed random vector conditioned on each hypothesis. However, for many app...
This document formalizes and discusses the implementation of a new, more efficient probabilistic plan recognition algorithm called Yet Another Probabilistic Plan Recognizer, (Yapp...
Christopher W. Geib, John Maraist, Robert P. Goldm...