Proceedings of the 2008 ACM symposium on Applied computing
A robust hidden Markov Gauss mixture vector quantizer for a noisy source
IEEE Transactions on Image Processing
Hierarchical multiple Markov chain model for unsupervised texture segmentation
IEEE Transactions on Image Processing
Image segmentation based on Gabor filter and MRF model
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
A conditional random field approach to unsupervised texture image segmentation
EURASIP Journal on Advances in Signal Processing
International Journal of Computer Vision
Markov random fields for improving 3D mesh analysis and segmentation
EG 3DOR'08 Proceedings of the 1st Eurographics conference on 3D Object Retrieval
A plausible texture enlargement and editing compound Markovian model
MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
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Markov random field (MRF) theory has been widely applied to the challenging problem of image segmentation. In this paper, we propose a new nontexture segmentation model using compound MRFs, in which the original label MRF is coupled with a new boundary MRF to help improve the segmentation performance. The boundary model is relatively general and does not need prior training on boundary patterns. Unlike some existing related work, the proposed method offers a more compact interaction between label and boundary MRFs. Furthermore, our boundary model systematically takes into account all the possible scenarios of a single edge existing in a 3times3 neighborhood and, thus, incorporates sophisticated prior information about the relation between label and boundary. It is experimentally shown that the proposed model can segment objects with complex boundaries and at the same time is able to work under noise corruption. The new method has been applied to medical image segmentation. Experiments on synthetic images and real clinical datasets show that the proposed model is able to produce more accurate segmentation results and satisfactorily keep the delicate boundary. It is also less sensitive to noise in both high and low signal-to-noise ratio regions than some of the existing models in common use