<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Luc Van Gool</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Dense parameter fields from total least squares</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 24th DAGM Symposium on Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">LNCS 2449</style></volume><pages><style face="normal" font="default" size="100%">379--386</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A method for the interpolation of parameter fields estimated by total least squares is presented. This is applied to the study of dynamic processes where the motion and further values such as divergence or brightness changes are parameterised in a partial differential equation. For the regularisation we introduce a constraint that restricts the solution only in the subspace determined by the total least squares procedure. The performance is illustrated on both synthetic and real test data.</style></abstract><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record></records></xml>