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We introduce a convex relaxation framework to optimally minimize continuous surface ratios. The key idea is to minimize the continuous surface ratio by solving a sequence of convex optimization problems. We show that such minimal ratios are superior to traditionally used minimal surface formulations in that they do not suffer from a shrinking bias and no longer require the choice of a regularity parameter...
Estimating the camera pose in stereo vision systems is an important issue in computer vision and robotics. One popular way to handle this problem consists of determining the essential matrix which minimizes the algebraic error obtained from image point correspondences. Unfortunately, this search amounts to solving a nonconvex optimization, and the existing methods either rely on some approximations...
In this paper we propose a novel prior-based variational object segmentation method in a global minimization framework which unifies image segmentation and image denoising. The idea of the proposed method is to convexify the energy functional of the Chan-Vese method in order to find a global minimizer, so called continuous graph cuts. The method is extended by adding an additional shape constraint...
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