利用高阶似然进行立体重建

H. Jung, Kyoung Mu Lee, Sang Uk Lee
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引用次数: 11

摘要

在流行的贝叶斯方法下,一个立体问题可以通过定义可能性和先验来表述。可能性通常与一元项相关,先验由马尔可夫随机场(MRF)中的成对或高阶团定义。在本文中,我们提出在立体中使用高阶似然模型。许多传统的基于patch的匹配方法,如归一化互相关、拉普拉斯高斯或人口普查滤波器,都是在一个patch的所有像素具有相同差异的天真假设下设计的。然而,对于MRF,块成本可以表示为高阶团,因此匹配成本是图像块差异的函数。通过视差图从投影图像中获得的补丁应该提供更好的匹配,而不会在视差不连续处产生模糊效果。在基于补丁的高阶匹配成本中,人口普查过滤器方法可以很容易地简化为基于成对的小集团。基于普查滤波器的高阶似然模型的实验结果表明,高阶似然模型优于独立同分布一元模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stereo reconstruction using high order likelihood
Under the popular Bayesian approach, a stereo problem can be formulated by defining likelihood and prior. Likelihoods are often associated with unary terms and priors are defined by pair-wise or higher order cliques in Markov random field (MRF). In this paper, we propose to use high order likelihood model in stereo. Numerous conventional patch based matching methods such as normalized cross correlation, Laplacian of Gaussian, or census filters are designed under the naive assumption that all the pixels of a patch have the same disparities. However, patch-wise cost can be formulated as higher order cliques for MRF so that the matching cost is a function of image patch's disparities. A patch obtained from the projected image by a disparity map should provide a better match without the blurring effect around disparity discontinuities. Among patch-wise high order matching costs, the census filter approach can be easily reduced to pair-wise cliques. The experimental results on census filter-based high order likelihood demonstrate the advantages of high order likelihood over independent identically distributed unary model.
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