Destrempes François, Mignotte Max
Département d'Informatique et de Recherche Opérationnelle, C.P. 6128, Succ. Centre-Ville, Montréal, Quebec, Canada, H3C 3J7.
IEEE Trans Pattern Anal Mach Intell. 2004 May;26(5):626-38. doi: 10.1109/TPAMI.2004.1273940.
In this paper, we describe a statistical model for the gradient vector field of the gray level in images validated by different experiments. Moreover, we present a global constrained Markov model for contours in images that uses this statistical model for the likelihood. Our model is amenable to an Iterative Conditional Estimation (ICE) procedure for the estimation of the parameters; our model also allows segmentation by means of the Simulated Annealing (SA) algorithm, the Iterated Conditional Modes (ICM) algorithm, or the Modes of Posterior Marginals (MPM) Monte Carlo (MC) algorithm. This yields an original unsupervised statistical method for edge-detection, with three variants. The estimation and the segmentation procedures have been tested on a total of 160 images. Those tests indicate that the model and its estimation are valid for applications that require an energy term based on the log-likelihood ratio. Besides edge-detection, our model can be used for semiautomatic extraction of contours, localization of shapes, non-photo-realistic rendering; more generally, it might be useful in various problems that require a statistical likelihood for contours.
在本文中，我们描述了一种通过不同实验验证的图像灰度梯度向量场的统计模型。此外，我们提出了一种用于图像轮廓的全局约束马尔可夫模型，该模型将此统计模型用于似然性。我们的模型适用于用于参数估计的迭代条件估计（ICE）过程；我们的模型还允许通过模拟退火（SA）算法、迭代条件模式（ICM）算法或后验边缘模式（MPM）蒙特卡罗（MC）算法进行分割。这产生了一种用于边缘检测的原始无监督统计方法，有三种变体。估计和分割过程已在总共160张图像上进行了测试。这些测试表明，该模型及其估计对于需要基于对数似然比的能量项的应用是有效的。除了边缘检测之外，我们的模型还可用于轮廓的半自动提取、形状定位、非真实感渲染；更一般地说，它可能在各种需要轮廓统计似然性的问题中有用。
