A Novel Feature Point Matching Method for Skull. (May 2019)

• Record Type: Journal Article
• Authors: Jiabei, Hu; Wen, Yang; Shanghao, Zhao; Shixiong, Wang; Piao, Wang; Xiaoning, Liu
• Is Part Of: Journal of physics. Volume 1229(2019)
• Publication Date: 2019-05
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1229/1/012023
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Skull feature points play an important role in computer-aided craniofacial restoration. An improved relative angle histogram algorithm is proposed to match the feature points of the skull, aiming at the low positioning accuracy of the existing skull feature point matching algorithm and the difference of the number of points and the difference of the distribution of the model points. First, the Iterative Closest Point (ICP) algorithm is used to register the original skull model. Then, a new spindle is established for the model after registration. The relative angle of the model points is calculated and the phase diagonal distribution of the model points is calculated. Finally, the model points which are most similar to the histogram distribution of the model feature points are selected as the matching points. Experiments show that the algorithm has achieved good results in the matching of feature points of the skull model.