<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Benedek, Csaba</style></author><author><style face="normal" font="default" size="100%">Descombes, Xavier</style></author><author><style face="normal" font="default" size="100%">Zerubia, Josiane</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Building Extraction and Change Detection in Multitemporal Aerial and Satellite Images in a Joint Stochastic Approach</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">birth and death dynamics</style></keyword><keyword><style  face="normal" font="default" size="100%">building extraction</style></keyword><keyword><style  face="normal" font="default" size="100%">Change detection</style></keyword><keyword><style  face="normal" font="default" size="100%">marked point process</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2009</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://hal.inria.fr/inria-00426615</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this report we introduce a new probabilistic method which integrates building extraction with change detection in remotely sensed image pairs. A global optimization process attempts to find the optimal configuration of buildings, considering the observed data, prior knowledge, and interactions between the neighboring building parts. The accuracy is ensured by a Bayesian object model verification, meanwhile the computational cost is significantly decreased by a non-uniform stochastic object birth process, which proposes relevant objects with higher probability based on low-level image features.</style></abstract></record></records></xml>