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本文引用的文献

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Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.随机松弛，吉布斯分布，以及贝叶斯图像恢复。
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Stereo by intra- and inter-scanline search using dynamic programming.使用动态规划进行行间和行间搜索的立体匹配。
IEEE Trans Pattern Anal Mach Intell. 1985 Feb;7(2):139-54. doi: 10.1109/tpami.1985.4767639.
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Fusion moves for Markov random field optimization.融合动作的马尔可夫随机场优化。
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Global stereo reconstruction under second-order smoothness priors.全局立体重建的二阶平滑先验。
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A comparative study of energy minimization methods for Markov random fields with smoothness-based priors.基于平滑先验的马尔可夫随机场能量最小化方法的比较研究。
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Minimizing nonsubmodular functions with graph cuts - a review.基于图割的非次模函数最小化——综述
IEEE Trans Pattern Anal Mach Intell. 2007 Jul;29(7):1274-9. doi: 10.1109/TPAMI.2007.1031.
8
Probabilistic fusion of stereo with color and contrast for bilayer segmentation.用于双层分割的立体视觉与颜色和对比度的概率融合
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Efficient shape matching using shape contexts.使用形状上下文进行高效形状匹配。
IEEE Trans Pattern Anal Mach Intell. 2005 Nov;27(11):1832-7. doi: 10.1109/TPAMI.2005.220.
10
An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision.用于视觉能量最小化的最小割/最大流算法的实验比较。
IEEE Trans Pattern Anal Mach Intell. 2004 Sep;26(9):1124-37. doi: 10.1109/TPAMI.2004.60.

计算机视觉中的动态规划和图算法。

Dynamic programming and graph algorithms in computer vision.

机构信息

Department of Computer Science, University of Chicago, 1100 E. 58th St., Chicago, IL 60637, USA.

出版信息

IEEE Trans Pattern Anal Mach Intell. 2011 Apr;33(4):721-40. doi: 10.1109/TPAMI.2010.135.

DOI:10.1109/TPAMI.2010.135
PMID:20660950
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3717380/
Abstract

Optimization is a powerful paradigm for expressing and solving problems in a wide range of areas, and has been successfully applied to many vision problems. Discrete optimization techniques are especially interesting since, by carefully exploiting problem structure, they often provide nontrivial guarantees concerning solution quality. In this paper, we review dynamic programming and graph algorithms, and discuss representative examples of how these discrete optimization techniques have been applied to some classical vision problems. We focus on the low-level vision problem of stereo, the mid-level problem of interactive object segmentation, and the high-level problem of model-based recognition.

摘要

优化是一种在广泛领域中表达和解决问题的强大范例，并且已经成功应用于许多视觉问题。离散优化技术特别有趣，因为它们通过仔细利用问题结构，通常可以为解决方案质量提供非平凡的保证。在本文中，我们回顾了动态规划和图算法，并讨论了这些离散优化技术如何应用于一些经典视觉问题的代表性示例。我们专注于立体视觉的低级问题、交互式对象分割的中级问题和基于模型的识别的高级问题。