Stochastic models for generic images
Stochastic models for generic images
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DOI：
10.1090/qam/1811096
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发表时间：
2001-03-01
影响因子：
0.8
通讯作者：
Gidas, B
中科院分区：
文献类型：
--
作者：
Mumford, D;Gidas, B
1.导论.使用统计推断来分析和理解图像的想法已经使用了至少20年，例如，可以追溯到Grenander [Gr]和库珀[Co]的工作。为了应用这些技术，当然需要针对图像中存在的某类图像或某类结构的概率模型。已经推出了许多这种类型的模型。有图像纹理[GGGD]、[ZMW]、图像轮廓[Mu]、[GCK]、图像区域分解[GG]、[MS]、视差图、形状语法解析[Fu]、模板匹配和特定任务（如人脸识别[HGYGM]）的随机模型。所有这些研究的共同框架是通过一组辅助变量{xa}来描述某类图像I（x，y），所述辅助变量表示图像中的显著结构，例如，边缘、纹理统计、推断的深度值或关系、照明特征、中轴或形状特征、关键点的位置（例如，脸部中的眼睛）、标签（如在字符识别中）等。然后，定义i）”隐藏”变量p（{xQ}）的先验概率模型和ii）给定隐藏变量的i的成像模型p（I\{xQ}）。最后，使用贝叶斯规则p（{xa}\I）cxp（/|{：ra}）p（{：rQ}），其被应用于推断例如给定图像的隐变量的MAP估计。这种方法隐含的推论是，存在一个定义明确的边际分布
1. Introduction. The idea of using statistical inference for analyzing and understanding images has been used for at least 20 years, going back, for instance, to the work of Grenander [Gr] and Cooper [Co]. To apply these techniques, one needs, of course, a probabilistic model for some class of images or some class of structures present in images. Many models of this type have been introduced. There are stochastic models for image textures [GGGD],[ZMW], for contours in images [Mu],[GCK], for the decomposition of an image into regions [GG],[MS], for disparity maps, for grammatical parsing of shapes [Fu], for template matching, and for specific tasks such as face recognition [HGYGM]. The common framework for all these studies is to describe some class of images I (x, y) by means of a set of auxiliary variables {xa} representing the salient structures in the images, eg, edges, texture statistics, inferred depth values or relations, illumination features, medial axes or shape features, locations of key points such as eyes in a face, labels (as in character recognition), etc. Then i) a prior probability model for the" hidden" variables p ({xQ}) and ii) an imaging model p (I\{xQ}) for/, given the hidden variables, are defined. Finally, an image is analyzed using Bayes's rule p ({xa}\I) cxp (/|{: ra}) p ({: rQ}) which is applied to infer, eg, the MAP estimate for the hidden variables, given the image. Implicit in this approach is the deduction that there is a well-defined marginal distribution