Claudia Kondermann

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Confidence measures are crucial to the interpretation of any optical flow measurement. Even though numerous methods for estimating optical flow have been proposed over the last three decades, a sound, universal, and statistically motivated confidence measure for optical flow measurements is still missing. We aim at filling this gap with this contribution,(More)
The prevalence of diabetes is expected to increase dramatically in coming years; already today it accounts for a major proportion of the health care budget in many countries. Diabetic Retinopathy (DR), a micro vascular complication very often seen in diabetes patients, is the most common cause of visual loss in working age population of developed countries(More)
Confidence measures are important for the validation of optical flow fields by estimating the correctness of each displacement vector. There are several frequently used confidence measures, which have been found of at best intermediate quality. Hence, we propose a new confidence measure based on linear subspace projections. The results are compared to the(More)
Dense optical flow fields are required for many applications. They can be obtained by means of various global methods which employ regularization techniques for propagating estimates to regions with insufficient information. However, incorrect flow estimates are propagated as well. We, therefore, propose surface measures for the detection of locations where(More)
An edge-sensitive variational approach for the restoration of optical flow fields is presented. Real world optical flow fields are frequently corrupted by noise, reflection artifacts or missing local information. Still, applications may require dense motion fields. In this paper, we pick up image inpainting methodology to restore motion fields, which have(More)
In algorithmic fluid flow estimation, regularization of neighboring flow vectors has become an important approach employing prior knowledge on resulting flow fields. Up to now, the main focus has been spatial regularization by means of either analytic terms or parametric models. In this paper we propose a new method for estimating flow fields using(More)
Linear inverse problems in computer vision, including motion estimation, shape fitting and image reconstruction, give rise to parameter estimation problems with highly correlated errors in variables. Established total least squares methods estimate the most likely corrections Acirc and bcirc to a given data matrix [A, b] perturbed by additive Gaussian(More)
The notion “Optical flow” refers to the apparent motion in the image plane produced by the projection of the real 3D motion onto the 2D image plane. The thesis at hand addresses postprocessing and restoration methods for arbitrarily computed optical flow fields. Many motion estimators have been proposed during the last three decades, but all of them suffer(More)
1 Institute for Mathematics, University of Potsdam, Am Neuen Palais 10, 14469 Potsdam, Germany {mkulesh,hols}@math.uni-potsdam.de 2 Institute for Numerical Simulation, University of Bonn, Wegelerstr. 6, 53115 Bonn, Germany {benjamin.berkels,nadine.olischlaeger,martin.rumpf}@ins.uni-bonn.de 3 Center of Industrial Mathematics (ZeTeM), University of Bremen,(More)
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