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Robust Tracking and Stereo Matching under Variable Illumination

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Robust Tracking and Stereo Matching under Variable Illumination
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.
Jingdan Zhang, Leonard McMillan, Jingyi Yu
Added 12 Oct 2009
Updated 28 Oct 2009
Type Conference
Year 2006
Where CVPR
Authors Jingdan Zhang, Leonard McMillan, Jingyi Yu
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