NDLI: Region disparity estimation and object segmentation based on graph cut and combination of multiple features

Content Provider	IEEE Xplore Digital Library
Author	Qiuyu Zhu Qiming Li Yuechuan Chen
Copyright Year	2012
Description	Author affiliation: Shanghai University, Shanghai, China (Qiuyu Zhu; Qiming Li) \|\| Shanghai Transportation Investment Information Technology CO., LTD, Shanghai, China (Yuechuan Chen)
Abstract	Moving objects segmentation is the foundation of intelligent moving body information collection. In the monocular vision system, it is too difficult to segment the foreground from background when their grayscale and color are similar. Compared with the grayscale and color, the scenery depth are less susceptible to external environment, if depth information can be got in stereo vision, it would be much easier to segment the foreground. Unfortunately, scenery accurate dense disparity map is often hard to get. In the paper, an optimizing method of calculating the dense disparity based on graph cut is used, and based on the dense disparity, the features of color and disparity are combined by graph cut to improve the accuracy of the image segmentation. The experimental results show that the method provides a better dense disparity map and also reduces the effect of light and strengthens the stability of segmentation by combining the features of color and disparity in graph cut optimization algorithm.
Starting Page	610
Ending Page	613
File Size	1129735
Page Count	4
File Format	PDF
ISBN	9781457721441
e-ISBN	9781457721434
DOI	10.1109/ICICIP.2012.6391542
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Image segmentation Image color analysis Clustering algorithms Object segmentation Gray-scale Stereo vision
Content Type	Text
Resource Type	Article

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