Analysis on Teeth Occlusion Distribution Based on Segmentation and Registration Algorithm

2021 ◽  
Author(s):  
Zihan Cao ◽  
Xinwu Sun ◽  
Shasha Liu ◽  
Gangyuan Chen ◽  
Yan Liu ◽  
...  
Keyword(s):  
Registration Algorithm

Research on the Topology Preservation of the Demons Non-rigid Registration Algorithm

2010 ◽  
Vol 36 (1) ◽  
pp. 179-183
Author(s):  
Xiang-Bo LIN ◽  
Tian-Shuang QIU ◽  
Su RUAN ◽  
NICOLIER Frédéric
Keyword(s):  
Topology Preservation ◽  
Rigid Registration ◽  
Registration Algorithm

Point Cloud Registration Algorithm Combined Gaussian Mixture Model and Point-to-Plane Metric

2018 ◽  
Vol 30 (4) ◽  
pp. 642
Author(s):  
Guichao Lin ◽  
Yunchao Tang ◽  
Xiangjun Zou ◽  
Qing Zhang ◽  
Xiaojie Shi ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Point Cloud ◽  
Gaussian Mixture ◽  
Point Cloud Registration ◽  
Registration Algorithm

scholarly journals OICP: An Online Fast Registration Algorithm Based on Rigid Translation Applied to Wire Arc Additive Manufacturing of Mold Repair

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1563
Author(s):  
Ruibing Wu ◽  
Ziping Yu ◽  
Donghong Ding ◽  
Qinghua Lu ◽  
Zengxi Pan ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Point Cloud ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Multiple Point ◽  
Icp Algorithm ◽  
Registration Algorithm ◽  
Cloud Models ◽  
Comparison Results ◽  
Wire Arc Additive Manufacturing

As promising technology with low requirements and high depositing efficiency, Wire Arc Additive Manufacturing (WAAM) can significantly reduce the repair cost and improve the formation quality of molds. To further improve the accuracy of WAAM in repairing molds, the point cloud model that expresses the spatial distribution and surface characteristics of the mold is proposed. Since the mold has a large size, it is necessary to be scanned multiple times, resulting in multiple point cloud models. The point cloud registration, such as the Iterative Closest Point (ICP) algorithm, then plays the role of merging multiple point cloud models to reconstruct a complete data model. However, using the ICP algorithm to merge large point clouds with a low-overlap area is inefficient, time-consuming, and unsatisfactory. Therefore, this paper provides the improved Offset Iterative Closest Point (OICP) algorithm, which is an online fast registration algorithm suitable for intelligent WAAM mold repair technology. The practicality and reliability of the algorithm are illustrated by the comparison results with the standard ICP algorithm and the three-coordinate measuring instrument in the Experimental Setup Section. The results are that the OICP algorithm is feasible for registrations with low overlap rates. For an overlap rate lower than 60% in our experiments, the traditional ICP algorithm failed, while the Root Mean Square (RMS) error reached 0.1 mm, and the rotation error was within 0.5 degrees, indicating the improvement of the proposed OICP algorithm.

Research on registration algorithm based on feature points for human eye retina images

2009 ◽  
Author(s):  
Yuran Liu ◽  
Huizhen Yang ◽  
Liyun Su ◽  
Yudong Zhang ◽  
Xuejun Rao
Keyword(s):  
Feature Points ◽  
Human Eye ◽  
Registration Algorithm ◽  
Eye Retina ◽  
Retina Images

scholarly journals Real-Time Lung Tumor Tracking Using a CUDA Enabled Nonrigid Registration Algorithm for MRI

2020 ◽  
Vol 8 ◽  
pp. 1-8
Author(s):  
Nazanin Tahmasebi ◽  
Pierre Boulanger ◽  
Jihyun Yun ◽  
Gino Fallone ◽  
Michelle Noga ◽  
...  
Keyword(s):  
Real Time ◽  
Lung Tumor ◽  
Nonrigid Registration ◽  
Registration Algorithm ◽  
Tumor Tracking

scholarly journals Automatic PET-CT Image Registration Method Based on Mutual Information and Genetic Algorithms

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Martina Marinelli ◽  
Vincenzo Positano ◽  
Francesco Tucci ◽  
Danilo Neglia ◽  
Luigi Landini
Keyword(s):  
Mutual Information ◽  
Optimization Method ◽  
Global Maximum ◽  
Similarity Function ◽  
Spatial Transformation ◽  
Registration Method ◽  
Registration Algorithm ◽  
Positron Emission ◽  
Pet Ct ◽  
Artery Disease

Hybrid PET/CT scanners can simultaneously visualize coronary artery disease as revealed by computed tomography (CT) and myocardial perfusion as measured by positron emission tomography (PET). Manual registration is usually required in clinical practice to compensate spatial mismatch between datasets. In this paper, we present a registration algorithm that is able to automatically align PET/CT cardiac images. The algorithm bases on mutual information (MI) as registration metric and on genetic algorithm as optimization method. A multiresolution approach was used to optimize the processing time. The algorithm was tested on computerized models of volumetric PET/CT cardiac data and on real PET/CT datasets. The proposed automatic registration algorithm smoothes the pattern of the MI and allows it to reach the global maximum of the similarity function. The implemented method also allows the definition of the correct spatial transformation that matches both synthetic and real PET and CT volumetric datasets.

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