00371nas a2200097   4500008004100000245007400041210006900115260002900184300001400213856004600227       2004                        eng d00aColor textured image segmentation using a multi-layer Markovian model0 aColor textured image segmentation using a multilayer Markovian m  aMiskolctapolcacJan 2004  a152 - 158  uhttps://home.inf.u-szeged.hu/en/node/129701171nas a2200109   4500008004100000245008500041210006900126260001800195300001400213520078800227856004601015       2003                        eng d00aUnsupervised segmentation of color textured images using a multi-layer MRF model0 aUnsupervised segmentation of color textured images using a multi  bIEEEc2003///  a961 - 9643 a<p>Herein, we propose a novel multi-layer Markov random field (MRF) image segmentation model which aims at combining color and texture features: Each feature is associated to a so called feature layer, where an MRF model is defined using only the corresponding feature. A special layer is assigned to the combined MRF model. This layer interacts with each feature layer and provides the segmentation based on the combination of different features. The model is quite generic and isn't restricted to a particular texture feature. Herein we will test the algorithm using Gabor and MRSAR texture features. Furthermore, the algorithm automatically estimates the number of classes at each layer (there can be different classes at different layers) and the associated model parameters.</p>  uhttps://home.inf.u-szeged.hu/en/node/123401156nas a2200109   4500008004100000245007400041210006900115260003500184300001400219520076700233856004601000       2002                        eng d00aMulticue MRF image segmentation: Combining texture and color features0 aMulticue MRF image segmentation Combining texture and color feat  bIEEE Computer Societyc2002///  a660 - 6633 a<p>Herein, we propose a new Markov random field (MRF) image segmentation model which aims at combining color and texture features. The model has a multi-layer structure: Each feature has its own layer, called feature layer, where an MRF model is defined using only the corresponding feature. A special layer is assigned to the combined MRF model. This layer interacts with each feature layer and provides the segmentation based on the combination of different features. The uniqueness of our algorithm is that it provides both color only and texture only segmentations as well as a segmentation based on combined color and texture features. The number of classes on feature layers is given by the user but it is estimated on the combined layer. © 2002 IEEE.</p>  uhttps://home.inf.u-szeged.hu/en/node/1246