0343251 20240111140739.920100617235959.9 4 s. www cav_un_epca*0343250978-1-4244-7166-9369-372Los AlamitosIEEE2010 Range segmentation Markov random fields cav_un_auth*0101093 Haindl Michal Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101239 Žid Pavel Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0242154 Holub Radek Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. pdf http://library.utia.cas.cz/separaty/2010/RO/haindl-range video segmentation.pdf 1M0572 GA MŠk cav_un_auth*0001814 2C06019 GA MŠk CZ cav_un_auth*0216518 GA102/08/0593 GA ČR cav_un_auth*0239567 CEZ:AV0Z10750506 An unsupervised range video segmentation method based on a spatial probabilistic model for intended vehicle-based safety and warning system applications is introduced. Statistical range data discontinuities are represented by a wide-sense Markov model which guides the subsequent line-based region growing process. Single frame segmentations are mutually corrected using the continuity constraint. The resulting segmentation allows tracking moving objects and estimating their distance and velocity. The method is illustrated on synthetic range video data. cav_un_auth*0262028 10th International Conference on Information Sciences, Signal Processing and their Applications Kuala Lumpur 10.05.2010-13.05.2010 MY 2011 BD http://hdl.handle.net/11104/0185769 2010 pdf cav_un_epca*0343250 10th International Conference on Information Sciences, Signal Processing and their Applications 978-1-4244-7166-9 369 372 Los Alamitos IEEE 2010