%0 Journal Article %J IMAGE AND VISION COMPUTING %D 2006 %T A Markov random field image segmentation model for color textured images %A Zoltan Kato %A Ting Chuen Pong %B IMAGE AND VISION COMPUTING %V 24 %P 1103 - 1114 %8 2006/// %@ 0262-8856 %G eng %N 10 %! IMAGE VISION COMPUT %0 Book Section %B COMPUTER VISION - ACCV 2006, PT II %D 2006 %T A multi-layer MRF model for video object segmentation %A Zoltan Kato %A Ting Chuen Pong %E P J Narayanan %E S K Nayar %E H Y Shum %B COMPUTER VISION - ACCV 2006, PT II %I Springer Verlag %P 953 - 962 %8 2006/// %G eng %0 Book Section %B Joint Hungarian-Austrian conference on image processing and pattern recognition. 5th conference of the Hungarian Association for Image Processing and Pattern Recognition (KÉPAF), 29th workshop of the Austrian Association for Pattern Reco %D 2005 %T Video Object Segmentation Using a Multicue Markovian Model %A Zoltan Kato %A Ting Chuen Pong %E Dmitrij Chetverikov %E László Czúni %E Markus Vincze %B Joint Hungarian-Austrian conference on image processing and pattern recognition. 5th conference of the Hungarian Association for Image Processing and Pattern Recognition (KÉPAF), 29th workshop of the Austrian Association for Pattern Reco %I OCG %C Vienna %P 111 - 118 %8 2005/// %G eng %0 Conference Paper %B A Képfeldolgozók és Alakfelismerők Társaságának konferenciája - KÉPAF 2004 %D 2004 %T Color textured image segmentation using a multi-layer Markovian model %A Zoltan Kato %A Ting Chuen Pong %A Song Guo Qiang %B A Képfeldolgozók és Alakfelismerők Társaságának konferenciája - KÉPAF 2004 %C Miskolctapolca %P 152 - 158 %8 Jan 2004 %G eng %0 Book Section %B ICIP 2003: IEEE International Conference on Image Processing %D 2003 %T Unsupervised segmentation of color textured images using a multi-layer MRF model %A Zoltan Kato %A Ting Chuen Pong %A Song Guo Qiang %E IEEE %X

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.

%B ICIP 2003: IEEE International Conference on Image Processing %I IEEE %P 961 - 964 %8 2003/// %G eng %0 Book Section %B Proceedings 16th International Conference on Pattern Recognition (ICPR 2002) %D 2002 %T Multicue MRF image segmentation: Combining texture and color features %A Zoltan Kato %A Ting Chuen Pong %A Song Guo Qiang %E Ranga Katsuri %E D Laurendeau %E Ching Y Suen %X

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.

%B Proceedings 16th International Conference on Pattern Recognition (ICPR 2002) %I IEEE Computer Society %P 660 - 663 %8 2002/// %G eng %0 Journal Article %J PATTERN RECOGNITION LETTERS %D 2001 %T Color image segmentation and parameter estimation in a markovian framework %A Zoltan Kato %A Ting Chuen Pong %A Chung-Mong J Lee %X

An unsupervised color image segmentation algorithm is presented, using a Markov random field (MRF) pixel classification model. We propose a new method to estimate initial mean vectors effectively even if the histogram does not have clearly distinguishable peaks. The only parameter supplied by the user is the number of classes. © 2001 Elsevier Science B.V. All rights reserved.

%B PATTERN RECOGNITION LETTERS %V 22 %P 309 - 321 %8 2001/// %@ 0167-8655 %G eng %N 3-4 %! PATTERN RECOGN LETT %0 Book Section %B Proceedings of International Conference on Computer Analysis of Images and Patterns (CAIP) %D 2001 %T A Markov Random Field Image Segmentation Model Using Combined Color and Texture Features %A Zoltan Kato %A Ting Chuen Pong %E Wladyslaw Skarbek %B Proceedings of International Conference on Computer Analysis of Images and Patterns (CAIP) %I Springer Verlag %C Berlin; Heidelberg %P 547 - 554 %8 2001/// %G eng %0 Book Section %B Proceedings of Asian Conference on Computer Vision (ACCV) %D 1998 %T Motion Compensated Color Video Classification Using Markov Random Fields %A Zoltan Kato %A Ting Chuen Pong %A John Chung Mong Lee %E Roland Chin %E Ting Chuen Pong %B Proceedings of Asian Conference on Computer Vision (ACCV) %I Springer Verlag %C Berlin; Heidelberg %P 738 - 745 %8 1998/// %G eng %0 Conference Paper %D 1997 %T Color Image Classification and Parameter Estimation in a Markovian FrameworkProceedings of Workshop on 3D Computer Vision %A Zoltan Kato %A Ting Chuen Pong %A John Chung Mong Lee %E Hung Tat Tsui %E Chi Kit Ronald Chung %P 75 - 79 %8 1997.05 %G eng %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.128.1560 %0 Generic %D 1997 %T Motion Compensated Color Image Classification and Parameter Estimation in a Markovian Framework %A Zoltan Kato %A Ting Chuen Pong %A John Chung Mong Lee %8 1997/// %G eng %U http://biblioteca.universia.net/html_bura/ficha/params/title/motion-compensated-color-image-classification-and-parameter-estimation-in-markovian/id/5664082.html