Accurate Integration of Multi-view Range Images Using K-Means Clustering

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dc.contributor.author Zhou, Hong
dc.contributor.author Liu, Yonghuai
dc.date.accessioned 2011-11-08T13:44:01Z
dc.date.available 2011-11-08T13:44:01Z
dc.date.issued 2008-01-01
dc.identifier.citation Zhou , H & Liu , Y 2008 , ' Accurate Integration of Multi-view Range Images Using K-Means Clustering ' Pattern Recognition , vol 41 , no. 1 , pp. 152-175 . en
dc.identifier.issn 0031-3203
dc.identifier.other PURE: 173270
dc.identifier.other dspace: 2160/7677
dc.identifier.uri http://hdl.handle.net/2160/7677
dc.description Zhou, H., Liu, Y. (2008). Accurate Integration of Multi-view Range Images Using K-Means Clustering. Pattern Recognition, 41 (1), 152-175 en
dc.description.abstract 3D modelling finds a wide range of applications in industry. However, due to the presence of surface scanning noise, accumulative registration errors, and improper data fusion, reconstructed object surfaces using range images captured from multiple viewpoints are often distorted with thick patches, false connections, blurred features and artefacts. Moreover, the existing integration methods are often expensive in the sense of both computational time and data storage. These shortcomings limit the wide applications of 3D modelling using the latest laser scanning systems. In this paper, the k-means clustering approach (from the pattern recognition and machine learning literatures) is employed to minimize the integration error and to optimize the fused point locations. To initialize the clustering approach, an automatic method is developed, shifting points in the overlapping areas between neighbouring views towards each other, so that the initialized cluster centroids are in between the two overlapping surfaces. This results in more efficient and effective integration of data. While the overlapping areas were initially detected using a single distance threshold, they are then refined using the k-means clustering method. For more accurate integration results, a weighting scheme reflecting the imaging principle is developed to integrate the corresponding points in the overlapping areas. The fused point set is finally triangulated using an improved Delaunay method, guaranteeing a watertight surface. A comparative study based on real images shows that the proposed algorithm is efficient in the sense of either running time or memory usage and reduces significantly the integration error, while desirably retaining geometric details of 3D object surfaces of interest. en
dc.format.extent 24 en
dc.language.iso eng
dc.relation.ispartof Pattern Recognition en
dc.subject 3D modelling en
dc.subject Registered range images en
dc.subject k-Means clustering en
dc.subject Point shift en
dc.subject Overlapping area detection and integration en
dc.subject Surface details en
dc.title Accurate Integration of Multi-view Range Images Using K-Means Clustering en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1016/j.patcog.2007.06.006
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Vision, Graphics and Visualisation Group en
dc.description.status Peer reviewed en


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