We propose a method to learn heterogeneous models of object classes for visual recognition. The training images contain a preponderance of clutter and learning is unsupervised. Ou...
In this paper we present a novel approach to estimate and analyze 3D uid structure and motion of clouds from multi-spectrum 2D cloud image sequences. Accurate cloud-top structure ...
Motivated by the success of parts based representations in face detection we have attempted to address some of the problems associated with applying such a philosophy to the task ...
We present Propagation Networks (P-Nets), a novel approach for representing and recognizing sequential activities that include parallel streams of action. We represent each activi...
Yifan Shi, Yan Huang, David Minnen, Aaron F. Bobic...
Johnson and Hebert's spin-images have been applied to the registration of range images and object recognition with much success because they are rotation, scale, and pose inv...