NDLI: Range image registration using hierarchical segmentation and clustering

Content Provider	IEEE Xplore Digital Library
Author	Yonghuai Liu Longzhuang Li Xianghua Xie Baogang Wei
Copyright Year	2009
Description	Author affiliation: Department of Computing Sciences Texas A and M University, Corpus Christi, TX 78412, USA (Longzhuang Li) \|\| Department of Computer Science, Swansea University, Swansea SA2 8PP, UK (Xianghua Xie) \|\| College of Computer Sciences, Zhejiang University Hangzhou 310027, P.R. China (Baogang Wei) \|\| Department of Computer Science, Aberystwyth University, Ceredigion SY23 3DB, UK (Yonghuai Liu)
Abstract	An accurate, robust, and automatic registration of overlapping range images is usually a pre-requisite step for range image analysis and applications. While accurate depiction of object geometry requires the increase of the resolutions of images and thus, the amount of data to process, an efficient processing of such data then usually becomes an issue. In this paper, we first employ the efficient tensor analysis and k means clustering methods to hierarchically segment and cluster the original range images into a small number of planar patches represented as the closest points in the original images to their centroids. Then an advanced ICP variant is adopted to register such closest points. Finally, another ICP variant is used to refine the registration results obtained over all the points in the images. The experimental results based on real range images show that the proposed technique significantly outperforms the selected two state of the art ones for accurate and efficient registration of overlapping range images.
Starting Page	328
Ending Page	333
File Size	1050421
Page Count	6
File Format	PDF
ISBN	9781424448081
DOI	10.1109/CIRA.2009.5423184
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-15
Publisher Place	Korea (South)
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image registration Image segmentation Cameras Feature extraction Computer science Laser modes Geometrical optics Optical sensors Optical noise Educational institutions
Content Type	Text
Resource Type	Article

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