TY - JOUR T1 - A Markov random field image segmentation model for color textured images JF - IMAGE AND VISION COMPUTING Y1 - 2006 A1 - Zoltan Kato A1 - Ting Chuen Pong VL - 24 SN - 0262-8856 IS - 10 N1 - UT: 000241228300006doi: 10.1016/j.imavis.2006.03.005 JO - IMAGE VISION COMPUT ER - TY - CHAP T1 - A multi-layer MRF model for video object segmentation T2 - COMPUTER VISION - ACCV 2006, PT II Y1 - 2006 A1 - Zoltan Kato A1 - Ting Chuen Pong ED - P J Narayanan ED - S K Nayar ED - H Y Shum JF - COMPUTER VISION - ACCV 2006, PT II PB - Springer Verlag N1 - UT: 000235773200095doi: 10.1007/11612704_95 ER - TY - CHAP T1 - Video Object Segmentation Using a Multicue Markovian Model T2 - 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 Y1 - 2005 A1 - Zoltan Kato A1 - Ting Chuen Pong ED - Dmitrij Chetverikov ED - László Czúni ED - Markus Vincze JF - 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 PB - OCG CY - Vienna ER - TY - CONF T1 - Color textured image segmentation using a multi-layer Markovian model T2 - A Képfeldolgozók és Alakfelismerők Társaságának konferenciája - KÉPAF 2004 Y1 - 2004 A1 - Zoltan Kato A1 - Ting Chuen Pong A1 - Song Guo Qiang JF - A Képfeldolgozók és Alakfelismerők Társaságának konferenciája - KÉPAF 2004 CY - Miskolctapolca ER - TY - CHAP T1 - Unsupervised segmentation of color textured images using a multi-layer MRF model T2 - ICIP 2003: IEEE International Conference on Image Processing Y1 - 2003 A1 - Zoltan Kato A1 - Ting Chuen Pong A1 - Song Guo Qiang ED - IEEE AB -

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.

JF - ICIP 2003: IEEE International Conference on Image Processing PB - IEEE N1 - ScopusID: 0344666539doi: 10.1109/ICIP.2003.1247124 ER - TY - CHAP T1 - Multicue MRF image segmentation: Combining texture and color features T2 - Proceedings 16th International Conference on Pattern Recognition (ICPR 2002) Y1 - 2002 A1 - Zoltan Kato A1 - Ting Chuen Pong A1 - Song Guo Qiang ED - Ranga Katsuri ED - D Laurendeau ED - Ching Y Suen AB -

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.

JF - Proceedings 16th International Conference on Pattern Recognition (ICPR 2002) PB - IEEE Computer Society N1 - ScopusID: 33751583776 ER - TY - JOUR T1 - Color image segmentation and parameter estimation in a markovian framework JF - PATTERN RECOGNITION LETTERS Y1 - 2001 A1 - Zoltan Kato A1 - Ting Chuen Pong A1 - Chung-Mong J Lee AB -

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.

VL - 22 SN - 0167-8655 IS - 3-4 N1 - UT: 000167983900005ScopusID: 0035272740doi: 10.1016/S0167-8655(00)00106-9 JO - PATTERN RECOGN LETT ER - TY - CHAP T1 - A Markov Random Field Image Segmentation Model Using Combined Color and Texture Features T2 - Proceedings of International Conference on Computer Analysis of Images and Patterns (CAIP) Y1 - 2001 A1 - Zoltan Kato A1 - Ting Chuen Pong ED - Wladyslaw Skarbek JF - Proceedings of International Conference on Computer Analysis of Images and Patterns (CAIP) PB - Springer Verlag CY - Berlin; Heidelberg N1 - doi: 10.1007/3-540-44692-3_66 ER - TY - CHAP T1 - Motion Compensated Color Video Classification Using Markov Random Fields T2 - Proceedings of Asian Conference on Computer Vision (ACCV) Y1 - 1998 A1 - Zoltan Kato A1 - Ting Chuen Pong A1 - John Chung Mong Lee ED - Roland Chin ED - Ting Chuen Pong JF - Proceedings of Asian Conference on Computer Vision (ACCV) PB - Springer Verlag CY - Berlin; Heidelberg N1 - doi: 10.1007/3-540-63930-6_189 ER - TY - CONF T1 - Color Image Classification and Parameter Estimation in a Markovian FrameworkProceedings of Workshop on 3D Computer Vision Y1 - 1997 A1 - Zoltan Kato A1 - Ting Chuen Pong A1 - John Chung Mong Lee ED - Hung Tat Tsui ED - Chi Kit Ronald Chung UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.128.1560 ER - TY - ABST T1 - Motion Compensated Color Image Classification and Parameter Estimation in a Markovian Framework Y1 - 1997 A1 - Zoltan Kato A1 - Ting Chuen Pong A1 - John Chung Mong Lee UR - http://biblioteca.universia.net/html_bura/ficha/params/title/motion-compensated-color-image-classification-and-parameter-estimation-in-markovian/id/5664082.html ER -