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...
The vast amount of information freely available on the Web constitutes a unparalleled resource for the automatic knoweledge discovery and learning. In this paper we propose a study...
Manual generation of training examples for supervised learning is an expensive process. One way to reduce this cost is to produce training instances that are highly informative. T...
Justus H. Piater, Edward M. Riseman, Paul E. Utgof...
The problem of clustering is considered, for the case when each data point is a sample generated by a stationary ergodic process. We propose a very natural asymptotic notion of co...
We present a real-time algorithm called compensated ray marching for rendering of smoke under dynamic low-frequency environment lighting. Our approach is based on a decomposition ...
Kun Zhou, Zhong Ren, Stephen Lin, Hujun Bao, Baini...