NDLI: A stereo matching algorithm for lunar rover

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2010 2nd International Conference on Computer Engineering and Technology

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An IBE based fast authentication among WLANs for 3G users

Cluster-based routing using Bacteriologic Algorithm in Wireless Sensor Network

An analysis of the improved EAP-AKA protocol

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2009 International Conference on Computer Engineering and Technology

A stereo matching algorithm for lunar rover

Content Provider	IEEE Xplore Digital Library
Author	Fengping Cao Rongben Wang
Copyright Year	2010
Description	Author affiliation: Intelligent Vehicle Group, Transportation College, Jilin University, Changchun, China (Fengping Cao; Rongben Wang)
Abstract	In order to satisfy the requirement of navigation and localization of the lunar rover, a new quasi-dense matching algorithm based on SIFT features and region growing is presented in the paper. Firstly, the original images are preprocessed by Gauss filter and CLAHE method to weaken the effect of noise and contrast, then the invariant SIFT feature points in each image are extracted and reliably matched with common constraints in stereo matching, such as epipolar constraint, uniqueness constraint and continuity constraint. Finally, matched SIFT features with high accuracy are taken as the seeds from which corresponding relations propagate towards other regions of the image by best first strategy. Experimental results indicate that the algorithm performs well when used in the matching of stereo image pair of simulated lunar surface terrain environment and produces a dense disparity map with rather high accuracy.
File Size	1288733
File Format	PDF
ISBN	9781424463473
e-ISBN	9781424463497
DOI	10.1109/ICCET.2010.5485954
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-04-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Navigation Moon Educational institutions Image reconstruction Intelligent transportation systems quasi-dense matching Filters Intelligent vehicles stereo matching Feature extraction SIFT features Stereo vision region growing lunar rover Image enhancement
Content Type	Text
Resource Type	Article

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