Abstract. The availability of ground-truth flow field is crucial for quantitative evaluation of any optical flow computation method. The fidelity of test data is also important...
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...
We present experimentally-acquired MR image sequence reconstruction results and a review of the recently proposed doubly adaptive temporal update method (DATUM) for the acquisitio...
William Scott Hoge, Lawrence P. Panych, Dana H. Br...
Current models for the learning of feature detectors work on two time scales: on a fast time scale the internal neurons' activations adapt to the current stimulus; on a slow ...
We present a new shape-from-distortion framework for recovering specular (reflective/refractive) surfaces. While most existing approaches rely on accurate correspondences between 2...