0447050 20240103210528.520150922235959.9 000364705500022 84945931089 10.1007/978-3-319-23192-1_22 13 s. P cav_un_epca*0447047978-3-319-23192-10302-9743I9256261-273SwitzerlandSpringer International Publishing2015AzzopardiGeorgePetkovNicolai Unsupervised image segmentation Textural features Illumination invariants Surface reflectance field Bidirectional texture function 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*0101165 Mikeš Stanislav 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*0021088 Kudo M. JP http://library.utia.cas.cz/separaty/2015/RO/haindl-0447050.pdf GA14-10911S GA ČR CZ cav_un_auth*0303439 An unsupervised, illumination invariant, multi-spectral, mul/-ti-resolution, multiple-segmenter for textured images with unknown number of classes is presented. The segmenter is based on a weighted combination of several unsupervised segmentation results, each in different resolution, using the modified sum rule. Multi-spectral textured image mosaics are locally represented by eight causal directional multi-spectral random field models recursively evaluated for each pixel. The single-resolution segmentation part of the algorithm is based on the underlying Gaussian mixture model and starts with an over segmented initial estimation which is adaptively modified until the optimal number of homogeneous texture segments is reached. The performance of the presented method is extensively tested on the Prague segmentation benchmark both on the surface reflectance field textures as well as on the static colour textures using the commonest segmentation criteria and compares favourably with several leading alternative image segmentation methods. cav_un_auth*0319317 16th International Conference on Computer Analysis of Images and Patterns Valletta 02.09.2015-04.09.2015 MT 2016 BD 3 PO RVO:67985556 http://hdl.handle.net/11104/0249426 cav_un_auth*0299017 Hokkaido University JP S 0.329 Q2 Q2 2015 84945931089 SCOPUS 000364705500022 WOS cav_un_epca*0447047 Computer Analysis of Images and Patterns - CAIP 2015 I 978-3-319-23192-1 0302-9743 261 273 Computer Analysis of Images and Patterns - CAIP 2015 Switzerland Springer International Publishing 2015 Lecture Notes in Computer Science 9256 Azzopardi George 340 Petkov Nicolai 340