纹理图像具有微观不规则但宏观存在某种统计规律性的特点.在图像分割中,为了捕捉此特性来改善分割效果,提出了EHMM-HMT(enhanced hidden Markov model-hidden Markov tree)和MSWHMT(multi-states weighted hidden Markov tree)模型的多尺度贝叶斯纹理图像分割方法.该方法通过EHMM模型有效地描述了图像块间的相互作用关系,在最粗尺度上并运用EHMM-HMT模型得到了有利于保持区域一致性的初分割.然后,为了减少初分割对边界

Texture images have abnormal, microscopic characteristics, but some parts of the image maintain statistical regularity from a macroscopic point of view. In order to capture these characteristics that improve image segmentation results, a new wavelet-based, multi-scale Bayesian texture image segmentation method, based on EHMM-HMT (enhanced hidden Markov model-hidden Markov tree) and MSWHMT (multi-states weighted hidden Markov tree) modes, is proposed. The image blocks’ relative interactions are described through the EHMM model effectively, and the homogenous raw segmentation, propitious to final fusion, is obtained on the coarsest scale. Subsequently, in order to reduce mislabeling the boundaries of raw segmentation and to decrease the computing complexity of the model, the MSWHMT model is proposed with better raw segmentations of high accurate boundary detection put on finer scales. Finally, a pixel level segmentation is reached through a multi-scale Bayesian fusing strategy that combines with the boundaries. The method is compared to HMTseg, HMT (boundar based+MAP), and EHMM-HMT (MAP) algorithm through several micro-texture images to demonstrate its competitive performance. It has also been found to improve the accuracy of macro-texture image segmentations.

Supported by the National Natural Science Foundation of China under Grant Nos.60673097, 60601029, 60672126, 60702062 (国家自然科学基金); the National High-Tech Research and Development Plan of China under Grant Nos.2008AA01Z125, 2007AA12Z136, 2007AA12Z223 (国家高技术研