一种低复杂度二维隐Markov模型及其在图像分割中的应用

A Low Complexity 2D Hidden Markov Model in Application to Image Segmentation

该文提出了一种通用的低复杂度二维隐Markov模型,推导了前向算法和后向算法的递归形式。文中没有使用前人为了降低时间复杂度而提出的相邻图像块间条件独立性假设,使提出的模型更加通用,并且可以根据需要调节水平和竖直两个方向信息的权重,具有更高的灵活性。将该模型应用于图像分割,实验结果证明了模型的有效性。

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.