<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Preusser, T.</style></author><author><style face="normal" font="default" size="100%">Marc Droske</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Martin Rumpf</style></author><author><style face="normal" font="default" size="100%">Alexandru Telea</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A phase field method for joint denoising, edge detection, and motion estimation in image sequence processing.</style></title><secondary-title><style face="normal" font="default" size="100%">SIAM Journal of Applied Mathematics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">68</style></volume><pages><style face="normal" font="default" size="100%">599-618</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The estimation of optical flow fields from image sequences is incorporated in a Mumford/Shah approach for image denoising and edge detection. Possibly noisy image sequences are considered as input and a piecewise smooth image intensity, a piecewise smooth motion field, and a joint discontinuity set are obtained as minimizers of the functional. The method simultaneously detects image edges and motion field discontinuities in a rigorous and robust way. It is able to handle information on motion that is concentrated on edges. Inherent to it is a natural multiscale approximation that is closely related to the phase field approximation for edge detection by Ambrosio and Tortorelli. We present an implementation for two-dimensional image sequences with finite elements in space and time. This leads to three linear systems of equations, which have to be solved in a suitable iterative minimization procedure. Numerical results and different applications underline the robustness of the approach presented.</style></abstract></record></records></xml>