Mathematical Models in Computer Vision: Graph Cut Algorithms for Binocular Stereo with Occlusions

ABSTRACT: Most binocular stereo algorithms assume that all scene elements are visible from both cameras. Scene elements that are visible from only one camera, known as occlusions, pose an important challenge for stereo. Occlusions are important for segmentation, because they appear near discontinuities. However, stereo algorithms tend to ignore occlusions because of their difficulty. One reason is that occlusions require the input images to be treated symmetrically, which complicates the problem formulation. Worse, certain depth maps imply physically impossible scene configurations, and must be excluded from the output.

In this chapter we approach the problem of binocular stereo with occlusions from an energy minimization viewpoint. We begin by reviewing traditional stereo methods that do not handle occlusions. If occlusions are ignored, it is easy to formulate the stereo problem as a pixel labeling problem, which leads to an energy function that is common in early vision. This kind of energy function can be minimized using graph cuts, which is a combinatorial optimization technique that has proven to be very effective for low-level vision problems. Motivated by this, we have designed two graph cut stereo algorithms that are designed to handle occlusions. These algorithms produce promising experimental results on real data with ground truth.

	Main		Mathematical Models in Computer Vision: The Handbook, Springer (2005)
	Editors Preface Contents Contributors References Sample Chapter Order		Graph Cut Algorithms for Binocular Stereo with Occlusions, V. Kolmogorov and R. Zabih ABSTRACT: Most binocular stereo algorithms assume that all scene elements are visible from both cameras. Scene elements that are visible from only one camera, known as occlusions, pose an important challenge for stereo. Occlusions are important for segmentation, because they appear near discontinuities. However, stereo algorithms tend to ignore occlusions because of their difficulty. One reason is that occlusions require the input images to be treated symmetrically, which complicates the problem formulation. Worse, certain depth maps imply physically impossible scene configurations, and must be excluded from the output. In this chapter we approach the problem of binocular stereo with occlusions from an energy minimization viewpoint. We begin by reviewing traditional stereo methods that do not handle occlusions. If occlusions are ignored, it is easy to formulate the stereo problem as a pixel labeling problem, which leads to an energy function that is common in early vision. This kind of energy function can be minimized using graph cuts, which is a combinatorial optimization technique that has proven to be very effective for low-level vision problems. Motivated by this, we have designed two graph cut stereo algorithms that are designed to handle occlusions. These algorithms produce promising experimental results on real data with ground truth.

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