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Multiple helical scans and the reconstruction of over FOV-sized objects in cone-beam CT 被引量:1
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作者 韩玉 闫镔 +3 位作者 李磊 宇超群 李建新 包尚联 《Chinese Physics B》 SCIE EI CAS CSCD 2012年第6期588-594,共7页
In cone-beam computed tomography (CBCT), there are often cases where the size of the specimen is larger than the field of view (FOV) (referred to as over FOV-sized (OFS)). To acquire the complete projection da... In cone-beam computed tomography (CBCT), there are often cases where the size of the specimen is larger than the field of view (FOV) (referred to as over FOV-sized (OFS)). To acquire the complete projection data for OFS objects, some scan modes have been developed for long objects and short but over-wide objects. However, these modes still cannot meet the requirements for both longitudinally long and transversely wide objects. In this paper, we propose a multiple helical scan mode and a corresponding reconstruction algorithm for both longitudinally long and transversely wide objects. The simulation results show that our model can deal with the problem and that the results are acceptable, while the OFS object is twice as long compared with the FOV in the same latitude. 展开更多
关键词 large object multiple helical scans reconstruction algorithm cone-beam computed to-mography
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Point cloud simplification algorithm based on particle swarm optimization for online measurement of stored bulk grain 被引量:2
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作者 Shao Qing Xu Tao +3 位作者 Yoshino Tatsuo Zhao Yujie Yang Wenting Zhu Hang 《International Journal of Agricultural and Biological Engineering》 SCIE EI CAS 2016年第1期71-78,共8页
The simplification of 3D laser scanning point cloud is an important step of surface reconstruction and volume estimation of bulk grain in granary.This study presented an adaptive simplification algorithm based on part... The simplification of 3D laser scanning point cloud is an important step of surface reconstruction and volume estimation of bulk grain in granary.This study presented an adaptive simplification algorithm based on particle swarm optimization(PSO).It introduced PSO into the average distance method,a conventional simplification method.The basic idea of this algorithm was to adaptively determine the optimal point reducing intervals of scanning lines according to original point cloud density by PSO.By using the 3D point cloud scanned from bulk grain surface in granary,the proposed algorithm was validated.Compared with the average distance method,the proposed algorithm obtained more evenly distributed point set,smaller reduction ratio(6.96%)and higher volume estimation accuracy(relative error was less than 3‰).The 3D laser scanner(GSLS003,Jilin University and SkyViTech Co.,Ltd.,Hangzhou,China)used in this study could scan the complete picture of the grain surface in a granary in one time,so the acquired point cloud data do not have to be jointed.For the good simplification performance and capability of updating the reducing interval at any moment,the proposed algorithm and the 3D laser scanner could be used to realize online real-time measurement of stored bulk grain volume in granary. 展开更多
关键词 point cloud simplification algorithm particle swarm optimization(PSO) 3D laser scanning large object stored grain
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