Nonlinear total variation based noise removal algorithms
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SIAM Journal on Numerical Analysis
International Journal of Computer Vision
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Anisotropic geometric diffusion in surface processing
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Filtering, Segmentation, and Depth
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ACM Transactions on Graphics (TOG)
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International Journal of Computer Vision
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MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
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SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
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ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
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ACM SIGGRAPH 2003 Papers
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ACM Transactions on Graphics (TOG)
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Computational parametric Willmore flow
Numerische Mathematik
Variational Methods in Imaging
Variational Methods in Imaging
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We introduce two fourth-order regularization methods that remove geometric noise without destroying significant geometric features. These methods leverage ideas from image denoising and simplification of high contrast images in which piecewise affine functions are preserved up to infinitesimally small transition zones. We combine the regularization techniques with active contour models and apply them to segmentation of polygonal objects in aerial images. To avoid loss of features during the computation of the external driving forces we use total variation-based inverse scale-space techniques on the input data. Furthermore, we use the models for feature-preserving removal of geometric texture on surfaces.