@conference {1297,
	title = {Color textured image segmentation using a multi-layer Markovian model},
	booktitle = {A K{\'e}pfeldolgoz{\'o}k {\'e}s Alakfelismer{\H o}k T{\'a}rsas{\'a}g{\'a}nak konferenci{\'a}ja - K{\'E}PAF 2004},
	year = {2004},
	month = {Jan 2004},
	pages = {152 - 158},
	address = {Miskolctapolca}
}
@inbook {1234,
	title = {Unsupervised segmentation of color textured images using a multi-layer MRF model},
	booktitle = {ICIP 2003: IEEE International Conference on Image Processing},
	year = {2003},
	note = {ScopusID: 0344666539doi: 10.1109/ICIP.2003.1247124},
	month = {2003///},
	pages = {961 - 964},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {<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{\textquoteright}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>}
}
@inbook {1246,
	title = {Multicue MRF image segmentation: Combining texture and color features},
	booktitle = {Proceedings 16th International Conference on Pattern Recognition (ICPR 2002)},
	year = {2002},
	note = {ScopusID: 33751583776},
	month = {2002///},
	pages = {660 - 663},
	publisher = {IEEE Computer Society},
	organization = {IEEE Computer Society},
	abstract = {<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. {\textcopyright} 2002 IEEE.</p>}
}
