NDLI: Monocular depth cue fusion for image segmentation and grouping in outdoor navigation

Content Provider	IEEE Xplore Digital Library
Author	Wenhui Zhou Lili Lin Bin Lou Xuehui Wei
Copyright Year	2010
Description	Author affiliation: College of Computer and Software, Hangzhou Dianzi University, China (Wenhui Zhou; Bin Lou; Xuehui Wei) \|\| College of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou, China (Lili Lin)
Abstract	This paper proposes an efficient fusion strategy of monocular depth cue and other image features for natural image segmentation and grouping. The main idea is to improve the performance of image clustering via fusing depth cue, color, spatial location, and edge confidence in six-dimensional color-depth feature space. It integrates the monocular depth cue estimation, mean shift filtering and graph cuts algorithm together. Firstly, the dark channel prior based atmospheric transmission estimation is employed to recover monocular depth cue. Then the mean shift filtering in the weighted color-depth space is proposed to obtain cluster regions with correct boundaries. Finally, graph cuts algorithm is applied to achieve the final regional grouping. Experimental results indicate the proposed method has excellent performance in outdoor natural environments.
Starting Page	3201
Ending Page	3206
File Size	2206213
Page Count	6
File Format	PDF
ISBN	9781424466740
ISSN	21530866
e-ISBN	9781424466764
DOI	10.1109/IROS.2010.5649725
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-18
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pixel Image color analysis Image edge detection Roads Estimation Kernel Mathematical model
Content Type	Text
Resource Type	Article

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