Stereo Matching Algorithms with Different Cost Aggregation

被引：0

作者：

Ning, Kelin ^{[1
]}

Zhang, Xiaoying ^{[1
]}

Ming, Yue ^{[1
]}

机构：

[1] Beijing Polytech Univ, Beijing Key Lab Work Safety Intelligent Monitorin, Sch Elect Engn, U Beijing 100876, Peoples R China

来源：

PROCEEDINGS OF INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND INFORMATION TECHNOLOGY (CSAIT 2013) | 2014年 / 255卷

基金：

中国国家自然科学基金;

关键词：

Stereo match; Disparity image; Matching accuracy; Matching speed;

D O I：

10.1007/978-81-322-1759-6_74

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Stereo matching is one of the most active research fields in computer vision. The paper introduces the categories and the performance index of stereo matching and introduces three high-speed and state-of-the-art stereo matching algorithms with different cost aggregation: fast bilateral stereo (FBS), binary stereo matching (BSM), and a non-local cost aggregation method (NLCA). By comparing the performance in terms of both quality and speed, we concluded that FSB deals with the effects of noise well; BSM is suitable for embedded devices and has a good performance with radiometric differences; NLCA combines the efficiency with the accuracy of state-of-the-art algorithms.

引用

页码：647 / 653

页数：7

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