A convex optimization approach for depth estimation under illumination variation
IEEE Transactions on Image Processing
Robust fast belief propagation for real-time stereo matching
ICACT'09 Proceedings of the 11th international conference on Advanced Communication Technology - Volume 2
A three-stage approach to shadow field estimation from partial boundary information
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Visual assistance to an advanced mechatronic platform for pick and place tasks
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part II
Foreground prediction for bilayer segmentation of videos
Pattern Recognition Letters
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Illumination inconsistencies cause serious problems for classical computer vision applications such as tracking and stereo matching. We present a new approach to model illumination variations using an Illumination Ratio Map (IRM). An IRM computes the intensity ratio of corresponding points in an image pair. We formulate IRM recovery as a Markov network, which assumes spatially varying illumination changes can be modeled as a locally smooth function with boundaries. We show that the IRM Markov network can be easily incorporated into low-level vision problems, such as tracking and stereo matching, by integrating IRM estimation with the optical flow field/disparity map solution process. This leads to a unified Markov network. We develop an iterative optimization algorithm based on Belief Propagation to efficiently recover the illumination ratio map and the optical field/disparity map at the same time. Experiments demonstrate that our methods are robust and reliable.