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Your search keyword '"Geng, Guohua"' showing total 380 results
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380 results on '"Geng, Guohua"'

151. Valley-ridge feature extraction from point clouds

Author

张雨禾 Zhang Yu-he, 耿国华 Geng Guohua, and 魏潇然 Wei Xiao-ran

Subjects
Feature extraction, Point cloud, Geometry, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, Mathematics
Published
2015

152. Fast Projection Algorithm for LIM-Based Simultaneous Algebraic Reconstruction Technique and Its Parallel Implementation on GPU

Author

Biomedical Engineering and Mechanics, Zhang, Shunli, Geng, Guohua, Cao, Guohua, Zhang, Yuhe, Liu, Baodong, Dong, Xu, Biomedical Engineering and Mechanics, Zhang, Shunli, Geng, Guohua, Cao, Guohua, Zhang, Yuhe, Liu, Baodong, and Dong, Xu

Abstract

Simultaneous algebraic reconstruction technique (SART) is a well-known iterative method in X-ray computed tomography, which provides better image quality than analytical methods when dealing with incomplete or noisy data. The disadvantage of SART is the slow speed compared with the analytical methods. Since forward projection and backprojection are two major time-consuming operations in iterative reconstruction, we propose an algorithm for fast forward projection and improved backprojection for the line integral model-based SART. Using the proposed algorithm, the SART method was implemented on a GPU platform with NVIDIA's parallel computing architecture. Both computer simulations and physical phantom experiments were carried out, and their results show that our approach is highly efficient and accurate. The computation time for the system matrix using our proposed projector is 10 times faster than that using the Siddon's projector, and our improved backprojection algorithm is 1.5 times faster than Li's method in determining the minimum bounding interval. The GPU-based SART using our proposed projection algorithm can obtain about 7.4 times reconstruction speed-up compared with that using the traditional projection approach, while preserving the accuracy of the results.

Published
2018

153. Point Cloud Registration Algorithm Based on Extended Point Feature Histogram Feature

Author

周明全 Zhou Ming-quan, 汤慧 Tang Hui, and 耿国华 Geng Guohua

Subjects
Computer science, Feature (computer vision), business.industry, Histogram, Point cloud, Point (geometry), Pattern recognition, Artificial intelligence, Electrical and Electronic Engineering, business, Atomic and Molecular Physics, and Optics
Published
2019

166. Fast and Robust Reconstruction for Fluorescence Molecular Tomography via L1-2 Regularization

Author

Zhang, Haibo, Geng, Guohua, Wang, Xiaodong, Qu, Xuan, Hou, Yuqing, and He, Xiaowei

Subjects
Article Subject, lcsh:R, lcsh:Medicine
Abstract

Sparse reconstruction inspired by compressed sensing has attracted considerable attention in fluorescence molecular tomography (FMT). However, the columns of system matrix used for FMT reconstruction tend to be highly coherent, which means L1 minimization may not produce the sparsest solution. In this paper, we propose a novel reconstruction method by minimization of the difference of L1 and L2 norms. To solve the nonconvex L1-2 minimization problem, an iterative method based on the difference of convex algorithm (DCA) is presented. In each DCA iteration, the update of solution involves an L1 minimization subproblem, which is solved by the alternating direction method of multipliers with an adaptive penalty. We investigated the performance of the proposed method with both simulated data and in vivo experimental data. The results demonstrate that the DCA for L1-2 minimization outperforms the representative algorithms for L1, L2, L1/2, and L0 when the system matrix is highly coherent.

Published
2016

167. Low-Rank-Matrix-Completion-Based Method for Suppressing Background Fluorescence

Author

贺小伟 He Xiaowei, 易黄建 Yi Huang-jian, 王晓东 Wang Xiao-dong, 何雪磊 He Xuelei, and 耿国华 Geng Guohua

Subjects
Background fluorescence, Low-rank approximation, Algorithm, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, Mathematics
Published
2018

168. Fragment Splicing Method Based on Surface Texture Characteristic

Author

张雨禾 Zhang Yu-he, 王飘 Wang Piao, and 耿国华 Geng Guohua

Subjects
Crystallography, Fragment (computer graphics), Chemistry, RNA splicing, Surface finish, Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics
Published
2018

169. Location Method of Excitation Planes Based on Gaussian Mixture Distribution

Author

王晓东 Wang Xiao-dong, 易黄建 Yi Huang-jian, 耿国华 Geng Guohua, 何雪磊 He Xuelei, and 贺小伟 He Xiaowei

Subjects
Physics, Gaussian mixture distribution, Electrical and Electronic Engineering, Molecular physics, Atomic and Molecular Physics, and Optics, Excitation
Published
2018

170. Fracture Surface Matching Method for Terracotta Based on Feature Points

Author

赵夫群 Zhao Fuqun and 耿国华 Geng Guohua

Subjects
Feature (computer vision), business.industry, visual_art, Fracture (geology), visual_art.visual_art_medium, Pattern recognition, Artificial intelligence, Electrical and Electronic Engineering, Terracotta, business, Atomic and Molecular Physics, and Optics, Geology, Surface matching
Published
2018

175. Nonconvex L1-2 Regularization for Fast Cone-Beam X-Ray Luminescence Computed Tomography

Author

张海波 Zhang Hai-bo, 赵映程 Zhao Yingcheng, 易黄建 Yi Huang-jian, 侯榆青 Hou Yuqing, 孙 怡 Sun Yi, 贺小伟 He Xiaowei, and 耿国华 Geng Guohua

Subjects
Physics, medicine.medical_specialty, medicine.diagnostic_test, business.industry, X ray luminescence, Computed tomography, Regularization (mathematics), Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials, Optics, Cone (topology), medicine, Medical physics, business, Beam (structure)
Published
2017

180. Sparse non-convex Lp regularization for cone-beam X-ray luminescence computed tomography.

Author

Zhang, Haibo, Geng, Guohua, Zhang, Shunli, Li, Kang, Liu, Cheng, Hou, Yuqing, and He, Xiaowei

Subjects
*CONE beam computed tomography, *LUMINESCENCE, *COMPUTED tomography, *LAGRANGIAN functions, *CALCULUS of variations
Abstract

Cone-beam X-ray luminescence computed tomography (CB-XLCT) is an attractive hybrid imaging modality, and it has the potential of monitoring the metabolic processes of nanophosphors-based drugs in vivo. However, the XLCT imaging suffers from a severe ill-posed problem. In this work, a sparse nonconvex Lp (0 < p < 1) regularization was utilized for the efficient reconstruction for early detection of small tumour in CB-XLCT imaging. Specifically, we transformed the non-convex optimization problem into an iteratively reweighted scheme based on the L1 regularization. Further, an iteratively reweighted split augmented lagrangian shrinkage algorithm (IRW_SALSA-Lp) was proposed to efficiently solve the non-convex Lp (0 < p < 1) model. We studied eight different non-convex p-values (1/16, 1/8, 1/4, 3/8, 1/2, 5/8, 3/4, 7/8) in both 3D digital mouse experiments and in vivo experiments. The results demonstrate that the proposed non-convex methods outperform L2 and L1 regularization in accurately recovering sparse targets in CB-XLCT. And among all the non-convex p-values, our Lp(1/4 < p < 1/2) methods give the best performance. [ABSTRACT FROM AUTHOR]

Published
2018
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183. A System for Re-assembly of fragment Objects and Computer Aided Restoration of Cultural Relics

Author

Zhou, Mingquam, Geng, Guohua, Wu, Zhongke, Zheng, Xia, Shui, Wuyang, Lu, Ke, Gao, Yuan, Centre Européen de Réalité Virtuelle (CERV), École Nationale d'Ingénieurs de Brest (ENIB), National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University (BNU), Kyushu University [Fukuoka], Beihang University (BUAA), Robert Vergnieux, Caroline Delevoie, and delevoie, caroline

Subjects
[SHS.ARCHEO] Humanities and Social Sciences/Archaeology and Prehistory, [SHS.ARCHEO]Humanities and Social Sciences/Archaeology and Prehistory, [SHS.HIST] Humanities and Social Sciences/History, Assembly, Fragment, Matching, [SHS.HIST]Humanities and Social Sciences/History, Terra Cotta Warriors and Horses, Relics
Abstract

International audience; In this paper, a system for re-assembling broken objects is discussed. It includes feature contours extraction, matching and re-assembling of 3D complementary objects, etc. These results are applicable to the restoration of culture relics through computer techniques. A computer assisted restoration system of culture relics is developed, which has been applied to the restoration of Terra Cotta Warriors and Horses of Qin dynasty.

Published
2007

184. Fast and Robust Reconstruction for Fluorescence Molecular Tomography via L1-2 Regularization.

Author

Zhang, Haibo, Geng, Guohua, Wang, Xiaodong, Qu, Xuan, Hou, Yuqing, and He, Xiaowei

Subjects
*ANATOMY, *ANIMAL experimentation, *ANISOTROPY, *FLUORIMETRY, *MAGNETIC resonance imaging, *MICE, *OPTICAL tomography, *RESEARCH funding, *THREE-dimensional imaging
Abstract

Sparse reconstruction inspired by compressed sensing has attracted considerable attention in fluorescence molecular tomography (FMT). However, the columns of system matrix used for FMT reconstruction tend to be highly coherent, which means L1 minimization may not produce the sparsest solution. In this paper, we propose a novel reconstruction method by minimization of the difference of L1 and L2 norms. To solve the nonconvex L1-2 minimization problem, an iterative method based on the difference of convex algorithm (DCA) is presented. In each DCA iteration, the update of solution involves an L1 minimization subproblem, which is solved by the alternating direction method of multipliers with an adaptive penalty. We investigated the performance of the proposed method with both simulated data and in vivo experimental data. The results demonstrate that the DCA for L1-2 minimization outperforms the representative algorithms for L1, L2, L1/2, and L0 when the system matrix is highly coherent. [ABSTRACT FROM AUTHOR]

Published
2016
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186. Rigid blocks matching method based on contour curves and feature regions

Author

Zhao, Fuqun, Zhou, Mingquan, Geng, Guohua, and Zhu, Lipin

Abstract

This study proposes a blocks matching method based on contour curves and feature regions that improve the matching precision and speed with which rigid blocks with a specified thickness in point clouds are matched. The method comprises two steps: coarse matching and fine matching. In the coarse matching step, the rigid blocks are first segmented into a series of surfaces and the fracture surfaces are distinguished. Then, the contour curves of the fracture surfaces are extracted using an improved boundary growth method and the rigid blocks are coarsely matched with them. In the fine matching step, feature regions are first extracted from the fracture surfaces. Then, the centroid of each feature region is calculated and the fine matching of rigid blocks with the centroid sets is completed using an improved iterative closest point (ICP) algorithm. The improved ICP algorithm integrates the rotation angle constraint and dynamic iteration coefficient into a probability ICP algorithm, which significantly improves matching precision and speed. Experiments conducted using public blocks and Terracotta Warriors blocks indicate that the proposed method carries out rigid blocks matching more accurately and rapidly than various conventional methods.

Published
2018
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