Three Dimensional Posed Face Recognition with an Improved Iterative Closest Point Method

3D Research ◽  
2019 ◽  
Vol 10 (3-4) ◽  
Author(s):  
Shahram Mohammadi ◽  
Omid Gervei
Keyword(s):  
Face Recognition ◽  
Three Dimensional ◽  
Point Method ◽  
Iterative Closest Point ◽  
Closest Point Method

Point cloud registration based on improved iterative closest point method

2012 ◽  
Vol 20 (9) ◽  
pp. 2068-2076 ◽  
Author(s):  
王欣 WANG Xin ◽  
张明明 ZHANG Ming-ming ◽  
于晓 YU Xiao ◽  
章明朝 ZHANG Ming-chao
Keyword(s):  
Point Cloud ◽  
Point Method ◽  
Iterative Closest Point ◽  
Point Cloud Registration ◽  
Closest Point Method

Iterative Closest Point Method with a Least Trimmed Squares Estimator

2012 ◽  
Vol 220-223 ◽  
pp. 1381-1384
Author(s):  
Ying Yan ◽  
Qi Xia ◽  
Ke Wang
Keyword(s):  
Statistical Method ◽  
Point Method ◽  
Experimental Results ◽  
New Method ◽  
Iterative Closest Point ◽  
Least Trimmed Squares ◽  
Residual Distribution ◽  
Robust Statistical Method ◽  
Closest Point Method

The iterative closest point (ICP) method is one of the most important methods for 2D/3D point registration. Robust statistical method is applied widely for improving the robustness of ICP. A new method that incorporates the Least Trimmed Squares (LTS) Estimator into the ICP is proposed in this paper. In this method, outliers are removed according to characteristics of residual distribution. A large number of experimental results show that the proposed method is robust and efficient.

Merging the point clouds of the head and ear by using the iterative closest point method

2016 ◽  
Vol 1 (3) ◽  
pp. 305
Author(s):  
Ming Zhang ◽  
Roger Ball ◽  
Nathaniel J. Martin ◽  
Yan Luximon
Keyword(s):  
Point Clouds ◽  
Point Method ◽  
Iterative Closest Point ◽  
Closest Point Method

Bone Alignment Using the Iterative Closest Point Algorithm

2010 ◽  
Vol 26 (4) ◽  
pp. 526-530 ◽  
Author(s):  
Maarten Beek ◽  
Carolyn F. Small ◽  
Randy E. Ellis ◽  
Richard W. Sellens ◽  
David R. Pichora
Keyword(s):  
Three Dimensional ◽  
Carpal Bones ◽  
Iterative Closest Point ◽  
Computer Assisted ◽  
Surgical Interventions ◽  
Kinematic Study ◽  
Principal Axes ◽  
Closest Point Method ◽  
Surface Models ◽  
Iterative Closest Point Algorithm

Computer assisted surgical interventions and research in joint kinematics rely heavily on the accurate registration of three-dimensional bone surface models reconstructed from various imaging technologies. Anomalous results were seen in a kinematic study of carpal bones using a principal axes alignment approach for the registration. The study was repeated using an iterative closest point algorithm, which is more accurate, but also more demanding to apply. The principal axes method showed errors between 0.35 mm and 0.49 mm for the scaphoid, and between 0.40 mm and 1.22 mm for the pisiform. The iterative closest point method produced errors of less than 0.4 mm. These results show that while the principal axes method approached the accuracy of the iterative closest point algorithm in asymmetrical bones, there were more pronounced errors in bones with some symmetry. Principal axes registration for carpal bones should be avoided.

Novel method for rail wear inspection based on the sparse iterative closest point method

2017 ◽  
Vol 28 (12) ◽  
pp. 125201 ◽  
Author(s):  
Bing Yi ◽  
Yue Yang ◽  
Qian Yi ◽  
Wanlin Dai ◽  
Xiongbing Li
Keyword(s):  
Point Method ◽  
Iterative Closest Point ◽  
Rail Wear ◽  
Closest Point Method ◽  
Novel Method
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