We present a hierarchical generative model for object recognition that is constructed by weakly-supervised learning. A key component is a novel, adaptive patch feature whose width...
We propose statistical data association techniques for visual tracking of enormously large numbers of objects. We do not assume any prior knowledge about the numbers involved, and...
Margrit Betke, Diane E. Hirsh, Angshuman Bagchi, N...
In this paper, we propose a general framework for fusing bottom-up segmentation with top-down object behavior classification over an image sequence. This approach is beneficial fo...
We present a novel approach to real-time structured light range scanning. After an analysis of the underlying assumptions of existing structured light techniques, we derive a new ...
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...