NDLI: Three-dimensional geometric statistical model based on locating feature points of maximal curvature flow

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Content Provider	ACM Digital Library
Author	Chen, Zhang Jun-Sheng, Wu Yu, Hui Bin, Yu
Abstract	Aiming at the present problem that the spine and the part of the spine lack the sample library of geometric statistical model of different age groups, this paper studies a method to construct the three-dimensional geometric statistic model based on the locating feature points of maximum curvature flow. In this method, the 3D reconstructed human lumbar vertebrae model is adaptively identified and located based on the feature points of the normal curvature maxima, so as to the sample matrix is generated for each model. Then the improved ICP algorithm is used to align and register the sample matrix. Finally, the PCA (Principal Component Analysis) is used to train the model template after registration, in order to get the sample library of geometry statistical model of spine.
Starting Page	46
Ending Page	50
Page Count	5
File Format	PDF
ISBN	9781450348270
DOI	10.1145/3035012.3035016
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-01-06
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	3d model Maximal curvature flow Sample matrix Spine
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Three-dimensional geometric statistical model based on locating feature points of maximal curvature flow