Tiered Scene Labeling with Dynamic Programming

被引:31
|
作者
Felzenszwalb, Pedro F. [1 ]
Veksler, Olga [2 ]
机构
[1] Univ Chicago, Chicago, IL 60637 USA
[2] Univ Western Ontario, London, ON N6A 3K7, Canada
来源
2010 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) | 2010年
基金
美国国家科学基金会; 加拿大自然科学与工程研究理事会;
关键词
MARKOV RANDOM-FIELDS; ENERGY MINIMIZATION; OPTIMIZATION;
D O I
10.1109/CVPR.2010.5540067
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Dynamic programming (DP) has been a useful tool for a variety of computer vision problems. However its application is usually limited to problems with a one dimensional or low treewidth structure, whereas most domains in vision are at least 2D. In this paper we show how to apply DP for pixel labeling of 2D scenes with simple "tiered" structure. While there are many variations possible, for the applications we consider the following tiered structure is appropriate. An image is first divided by horizontal curves into the top, middle, and bottom regions, and the middle region is further subdivided vertically into subregions. Under these constraints a globally optimal labeling can be found using an efficient dynamic programming algorithm. We apply this algorithm to two very different tasks. The first is the problem of geometric class labeling where the goal is to assign each pixel a label such as "sky", "ground", and "surface above ground". The second task involves incorporating simple shape priors for segmentation of an image into the "foreground" and "background" regions.
引用
收藏
页码:3097 / 3104
页数:8
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