Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA 01003.
IEEE Trans Pattern Anal Mach Intell. 1987 Jan;9(1):39-55. doi: 10.1109/tpami.1987.4767871.
This paper presents a new approach to the use of Gibbs distributions (GD) for modeling and segmentation of noisy and textured images. Specifically, the paper presents random field models for noisy and textured image data based upon a hierarchy of GD. It then presents dynamic programming based segmentation algorithms for noisy and textured images, considering a statistical maximum a posteriori (MAP) criterion. Due to computational concerns, however, sub-optimal versions of the algorithms are devised through simplifying approximations in the model. Since model parameters are needed for the segmentation algorithms, a new parameter estimation technique is developed for estimating the parameters in a GD. Finally, a number of examples are presented which show the usefulness of the Gibbsian model and the effectiveness of the segmentation algorithms and the parameter estimation procedures.
本文提出了一种利用吉布斯分布（GD）对噪声和纹理图像进行建模和分割的新方法。具体来说，本文提出了基于 GD 层次结构的噪声和纹理图像数据的随机场模型。然后，本文提出了基于动态规划的噪声和纹理图像分割算法，考虑了统计最大后验（MAP）准则。然而，由于计算方面的考虑，通过简化模型中的近似，设计了算法的次优版本。由于分割算法需要模型参数，因此开发了一种新的参数估计技术来估计 GD 中的参数。最后，给出了一些示例，这些示例表明了 Gibbsian 模型以及分割算法和参数估计过程的有效性。
