Titlebook: Energy Minimization Methods in Computer Vision and Pattern Recognition; 10th International C Xue-Cheng Tai,Egil Bae,Marius Lysaker Conferen

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https://doi.org/10.1007/978-3-322-97023-7ally deform it to the actual task while tracking the solution. It was first used in computer vision under the name of graduated nonconvexity. Since then, it has been utilized explicitly or implicitly in various applications. In fact, state-of-the-art optical flow and shape estimation rely on a form

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Das Sprichwort im literarischen Text,els but also their values should be optimal to maximise the quality gain. The position of important data is usually encoded in a binary mask. Recent studies have shown that allowing non-binary masks may lead to tremendous speedups but comes at the expense of higher storage costs and yields prohibiti

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Marten Deinum,Daniel Rubio,Josh Longtches, treating the patches individually and averaging them back together. Recently, the Expected Patch Log Likelihood (EPLL) method has been introduced, arguing that the chosen model should be enforced on the final reconstructed image patches. In the context of a Gaussian Mixture Model (GMM), this

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https://doi.org/10.1007/978-1-4842-8649-4e consider a minimalistic formulation of the blind deconvolution problem where there are only two energy terms: a least-squares term for the data fidelity and an image prior based on a lower-bounded logarithm of the norm of the image gradients. We show that this energy formulation is sufficient to a