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Your search keyword '"Zhou, Mingquan"' showing total 711 results
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711 results on '"Zhou, Mingquan"'

154. A Hierarchical Skull Point Cloud Registration Method

Author

Yang Wen, Zhou Mingquan, Geng Guo-hua, and Liu Xiaoning

Subjects
feature matching, General Computer Science, Computer science, Physics::Medical Physics, 0211 other engineering and technologies, Point cloud, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 020101 civil engineering, 02 engineering and technology, spin image, 0201 civil engineering, k-nearest neighbors algorithm, Spatial reference system, 021105 building & construction, medicine, General Materials Science, Computer vision, geometric feature constraints, business.industry, random sampling, General Engineering, ICP algorithm, Skull, medicine.anatomical_structure, Skull registration, Computer Science::Computer Vision and Pattern Recognition, Artificial intelligence, lcsh:Electrical engineering. Electronics. Nuclear engineering, business, lcsh:TK1-9971
Abstract

Skull registration is one of the important steps in craniofacial reconstruction, and its registration accuracy and efficiency have an important impact on the reconstruction results. To solve the problem of low accuracy and efficiency of existing skull registration methods, a hierarchical skull point cloud registration method is proposed in this paper. The whole registration process is divided into a rough registration stage and a fine registration stage. Firstly, feature points are extracted from the pre-processed skull point cloud model, and a local coordinate reference system is established according to the feature points and their neighbor points. The improved spin image is used to construct the local feature descriptor. The feature matching is carried out according to the nearest neighbor algorithm, and the k-means algorithm is used to eliminate the mismatching points to achieve skull rough registration. Then, based on rough registration, we use an improved ICP algorithm to achieve fine registration of the skull. In this process, we use random sampling to reduce the search scale of points and add geometric feature constraints to further eliminate mismatched points. Finally, the whole registration algorithm is applied to the skull point cloud data to verify. The experimental results show that, compared with other methods, the registration effect and efficiency of the proposed method are superior to those of other methods. In order to verify the universality of the method, we also use a common data set for verification. Experiments show that the method is also very effective.

Published
2019

163. 基于无监督网络框架的文物点云模型去噪

Author

刘一萍 Liu Yiping, 周明全 Zhou Mingquan, 寇姣姣 Kou Jiaojiao, 鱼跃华 Yu Yuehua, 海琳琦 Hai Linqi, 李康 Li Kang, and 张海波 Zhang Haibo

Subjects
Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics
Published
2022

165. 基于数据增强的秦俑碎片深度分类模型

Author

鱼跃华 Yu Yuehua, 张海波 Zhang Haibo, 李昕 Li Xin, 寇姣姣 Kou Jiaojiao, 李康 Li Kang, 耿国华 Geng Guohua, and 周明全 Zhou Mingquan

Subjects
Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics
Published
2022

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