A REGION TREE BASED IMAGE DISCRETE LABELING FRAMEWORK

In this chapter, we present a region-tree based framework for general image discrete labeling problems. In particular, the input image is represented as a tree of over-segmented regions and the labeling problem is solved by optimizing an energy function defined on such a region-tree using dynamic programming (DP). By using the over-segmentation based region-tree as image representation, our framework combines the advantages of enabling labeling primitives that contain more information with a large support area compared to that of the pixel based approaches with a small support area and of reducing the risk of propagating segmentation errors into the labeling process compared to the layer based approaches. The region-tree representation further enables the use of fast DP optimization and enhances the performance by avoiding the use of smoothness constraints crossing discontinuities between adjacent labeling primitives. Variants such as coarse-to-fine region-tree and temporal region-tree are also developed for different applications such as binocular stereo matching, optical flow estimation and multi-view space-time consistent video depth recovery. The corresponding experimental evaluations have shown that our proposed framework is very effective and produces promising results.