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)
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 correctionsˆA andˆb to a given data matrix [A, b] perturbed by additive Gaussian noise, such(More)
Many problems in imaging are actually inverse problems. One reason for this is that conditions and parameters of the physical processes underlying the actual image acquisition are usually not known. Examples for this are the inhomo-geneities of the magnetic field in magnetic resonance imaging (MRI) leading
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