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RISS 인기검색어
- 검색키워드
- 저자 : Weihong Bi (검색결과 2 건)
- 무료
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- 유료
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A New Method of Point-Clouds Accurate Measurement and Reconstruction
Kun Zhang,Weihong Bi,Xinghu Fu 보안공학연구지원센터 2014 International Journal of Database Theory and Appli Vol.7 No.6
With the reverse engineering development, the accuracy of system is more important in reconstruction, especially in non-contacting measurement. This paper provides a new method measuring the accuracy the point clouds, define a image probability and the point probability according to uncertainty data. The quantity of the uncertain point data is important to measuring the result of reconstruction. The prior data can be catch from the last measurement process, especially the edge data or characteristic points. Referring to prior data, basing on the Bayesian theory the more accuracy posterior data can be computed in this paper. We divided the point cloud into different areas, and organized the data with hierarchical tree-structure. According to the probability of one tree node, we adjust the area corresponding to the node. At last, by using the existing experimental equipment, we verify the measurement of point cloud accuracy algorithm. The depth data was obtained by a laser scanner---SICK LMS100. The depth data can be computed as point data with uncertainty. The result of the reconstruction deeply relies on the quality of prior data.
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A New Kmeans Clustering Algorithm for Point Cloud
Kun Zhang,Weihong Bi,Xiaoming Zhang,Xinghu Fu,Li Zhu,Kunpeng Zhou 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.9
With development of 3D scanner, it becomes more convenient to access point data. However, for processing the large-scale point cloud, it raised a new challenge for computer graphics. This paper places an emphasis on the point data own characteristics, and then the point data have been divided into certain point sets by clustering algorithm, that is will be divided into different clusters. In order to suit for the point data organization or space division, the clustering algorithm would be improved. This paper provided a new Kmeans algorithm with density constraints. Before processing the point cloud by Kmeans algorithm with density constraints, the density of the point cloud have been defined in this paper, the density of the point cloud can be used for quantification of the convergence. Finally, the Kmeans algorithm with density constraints is verified by the experiment results. Our experiment showed that the improved Kmeans can reduce the processing time, especially, As the increase of the value of K, that is number of cluster, the calculating time of the clustering algorithm can be decreased greatly. In addition, with the increases of the the scale of data size, the stability of the improved Kmeans algorithm has been verified.
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