A Low Complexity 2D Hidden Markov Model in Application to Image Segmentation
一种低复杂度二维隐Markov模型及其在图像分割中的应用

The assumption of conditional independence in the relationship between adjacent blocks has been proposed by others to reduce the complexity of 2D HMM. In this paper, a more general 2D HMM relaxing this assumption is proposed. More general recursive forms of the forward and the backward algorithms are derived. And the model provides more flexibility by adjusting the weight between horizontal and vertical information. The application to image segmentation verifies the effectiveness of the model.