Benboudjema Dalila, Pieczynski Wojciech
INT/GET Départment CITI, CNRS UMR 5157, rue Charles Fourier, 9100 Evry, France.
IEEE Trans Pattern Anal Mach Intell. 2007 Aug;29(8):1367-78. doi: 10.1109/TPAMI.2007.1059.
Recent developments in statistical theory and associated computational techniques have opened new avenues for image modeling as well as for image segmentation techniques. Thus, a host of models have been proposed and the ones which have probably received considerable attention are the hidden Markov fields (HMF) models. This is due to their simplicity of handling and their potential for providing improved image quality. Although these models provide satisfying results in the stationary case, they can fail in the nonstationary one. In this paper, we tackle the problem of modeling a nonstationary hidden random field and its effect on the unsupervised statistical image segmentation. We propose an original approach, based on the recent triplet Markov field (TMF) model, which enables one to deal with nonstationary class fields. Moreover, the noise can be correlated and possibly non-Gaussian. An original parameter estimation method which uses the Pearson system to find the natures of the noise margins, which can vary with the class, is also proposed and used to perform unsupervised segmentation of such images. Experiments indicate that the new model and related processing algorithm can improve the results obtained with the classical ones.
统计理论及相关计算技术的最新进展为图像建模以及图像分割技术开辟了新途径。因此，人们提出了许多模型，其中可能受到广泛关注的是隐马尔可夫场（HMF）模型。这是因为它们处理起来很简单，并且有潜力提供更高的图像质量。尽管这些模型在平稳情况下能提供令人满意的结果，但在非平稳情况下可能会失效。在本文中，我们解决了对非平稳隐随机场进行建模及其对无监督统计图像分割的影响这一问题。我们基于最近提出的三元组马尔可夫场（TMF）模型提出了一种原创方法，该方法能够处理非平稳类场。此外，噪声可以是相关的，并且可能是非高斯的。我们还提出了一种原创的参数估计方法，该方法使用皮尔逊系统来确定噪声边界的性质，噪声边界可能因类别而异，并用于对此类图像进行无监督分割。实验表明，新模型和相关处理算法能够改善使用传统模型所获得的结果。
