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» Recovering Estimates of Fluid Flow from Image Sequence Data
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ECCV
2008
Springer
14 years 9 months ago
Learning Optical Flow
Assumptions of brightness constancy and spatial smoothness underlie most optical flow estimation methods. In contrast to standard heuristic formulations, we learn a statistical mod...
Deqing Sun, Stefan Roth, J. P. Lewis, Michael J. B...
CVPR
2011
IEEE
13 years 3 months ago
Probabilistic Simultaneous Pose and Non-Rigid Shape Recovery
We present an algorithm to simultaneously recover nonrigid shape and camera poses from point correspondences between a reference shape and a sequence of input images. The key nove...
Francesc Moreno (Institut de Robotica i Informatic...
CORR
2010
Springer
103views Education» more  CORR 2010»
13 years 7 months ago
Spatially-Adaptive Reconstruction in Computed Tomography Based on Statistical Learning
We propose a direct reconstruction algorithm for Computed Tomography, based on a local fusion of a few preliminary image estimates by means of a non-linear fusion rule. One such ru...
Joseph Shtok, Michael Zibulevsky, Michael Elad
ICIP
2007
IEEE
14 years 9 months ago
Isomap Tracking with Particle Filtering
The problem of tracking involves challenges like in-plane and out-of-plane rotations, scaling, variations in ambient light and occlusions. In this paper we look at the problem of ...
Nikhil Rane, Stanley T. Birchfield
ECCV
2006
Springer
14 years 9 months ago
Real-Time Non-rigid Shape Recovery Via Active Appearance Models for Augmented Reality
One main challenge in Augmented Reality (AR) applications is to keep track of video objects with their movement, orientation, size, and position accurately. This poses a challengin...
Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu