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...
Adaptive background modeling/subtraction techniques are popular, in particular, because they are able to cope with background variations that are due to lighting variations. Unfor...
Leonid Taycher, John W. Fisher III, Trevor Darrell
The paper describes a simple but robust framework for visual object tracking in a video sequence. Compared with the existing tracking techniques, our proposed tracking technique h...
This paper examines the problem of detecting changes in a 3-d scene from a sequence of images, taken by cameras with arbitrary but known pose. No prior knowledge of the state of n...
Recent work in object localization has shown that the use of contextual cues can greatly improve accuracy over models that use appearance features alone. Although many of these mo...
Carolina Galleguillos, Brian McFee, Gert Lanckriet