A framework for learning parameterized models of optical flow from image sequences is presented. A class of motions is represented by a set of orthogonal basis flow fields that ar...
Michael J. Black, Yaser Yacoob, Allan D. Jepson, D...
In this paper, we propose to use 3D information to augment the Markov random field (MRF) model for object recognition. Conventional MRF for image-based object recognition usually ...
Wei Yu, Ahmed Bilal Ashraf, Yao-Jen Chang, Congcon...
Markov Random Fields (MRFs) are an important class of probabilistic models which are used for density estimation, classification, denoising, and for constructing Deep Belief Netwo...
We propose in this paper a robust multi-resolution technique to estimate dense velocity field from image sequences. It couples a Gaussian pyramidal down-sampling decomposition to...
This paper treats automated detection of road and lane boundaries by fusing information from forwardlooking optical and active W-band radar imaging sensors mounted on a motor vehi...