0343263 20240111140739.920100617235959.9 20 s. www 524 cav_un_epca*0342819978-953-7619-90-9253-272Vukovar, CroatiaIn-Teh2010HeroutA. illumination invariants textural features Markov random fields cav_un_auth*0213290 Vácha 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*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. pdf http://library.utia.cas.cz/separaty/2010/RO/vacha-illumination invariants based on markov random fields.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 Content-based image retrieval systems (CBIR) typically query large image databases based on some automatically generated colour and textural features. Optimal robust features should be geometry and illumination invariant. Although image retrieval has been an active research area for many years this difficult problem is still far from being solved. We introduce fast and robust textural features that allow retrieving images with similar scenes comprising colour textured objects viewed with different illumination. The proposed textural features that are invariant to illumination spectrum and extremely robust to illumination direction. They require only a single training image per texture and no knowledge of illumination direction, brightness or spectrum. These feature utilises utilise illumination invariant features extracted from three different Markov random field (MRF) based texture representations. 2011 BD http://hdl.handle.net/11104/0185780 2010 pdf cav_un_epca*0342819 Pattern Recognition, Recent Advances 978-953-7619-90-9 253 272 Vukovar, Croatia In-Teh 2010 Herout A. 340