Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
3-D reconstruction of urban scenes from aerial stereo imagery: a focusing strategy
Computer Vision and Image Understanding
Total variation based convex filters for medical imaging
Applied Mathematics and Computation
Automatic Generation of High-Quality Building Models from Lidar Data
IEEE Computer Graphics and Applications
Structure-Texture Image Decomposition--Modeling, Algorithms, and Parameter Selection
International Journal of Computer Vision
SIAM Journal on Scientific Computing
Total variation minimization and a class of binary MRF models
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
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We present a semiautomatic approach to generate high quality digital terrain models (DTM) from digital surface models (DSM). A DTM is a model of the earths surface, where all man made objects and the vegetation have been removed. In order to achieve this, we use a variational energy minimization approach. The proposed energy functional incorporates Huber regularization to yield piecewise smooth surfaces and an L1 norm in the data fidelity term. Additionally, a minimum constraint is used in order to prevent the ground level from pulling up, while buildings and vegetation are pulled down. Being convex, the proposed formulation allows us to compute the globally optimal solution. Clearly, a fully automatic approach does not yield the desired result in all situations. Therefore, we additionally allow the user to affect the algorithm using different user interaction tools. Furthermore, we provide a real-time 3D visualization of the output of the algorithm which additionally helps the user to assess the final DTM. We present results of the proposed approach using several real data sets.