scholarly journals Rigid Shape Registration Based on Extended Hamiltonian Learning

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 539
Author(s):  
Jin Yi ◽  
Shiqiang Zhang ◽  
Yueqi Cao ◽  
Erchuan Zhang ◽  
Huafei Sun
Keyword(s):  
Image Analysis ◽  
Optimization Problem ◽  
Iterative Closest Point ◽  
Euclidean Group ◽  
Simulation Experiments ◽  
Icp Algorithm ◽  
Registration Error ◽  
Shape Registration ◽  
Correct Alignment ◽  
State Of Art

Shape registration, finding the correct alignment of two sets of data, plays a significant role in computer vision such as objection recognition and image analysis. The iterative closest point (ICP) algorithm is one of well known and widely used algorithms in this area. The main purpose of this paper is to incorporate ICP with the fast convergent extended Hamiltonian learning (EHL), so called EHL-ICP algorithm, to perform planar and spatial rigid shape registration. By treating the registration error as the potential for the extended Hamiltonian system, the rigid shape registration is modelled as an optimization problem on the special Euclidean group S E ( n ) ( n = 2 , 3 ) . Our method is robust to initial values and parameters. Compared with some state-of-art methods, our approach shows better efficiency and accuracy by simulation experiments.

Improvements to the Iterative Closest Point Algorithm for Shape Registration in Manufacturing

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Tsz-Ho Kwok ◽  
Kai Tang
Keyword(s):  
Stability Analysis ◽  
Selection Method ◽  
Iterative Closest Point ◽  
Normal Space ◽  
Point Selection ◽  
Icp Algorithm ◽  
Shape Registration ◽  
Normal Vectors ◽  
Iterative Closest Point Algorithm ◽  
The Stability

Iterative closest point (ICP) is a popular algorithm used for shape registration while conducting inspection during a production process. A crucial key to the success of the ICP is the choice of point selection method. While point selection can be customized for a particular application using its prior knowledge, normal-space sampling (NSS) is commonly used when normal vectors are available. Normal-based approach can be further improved by stability analysis—called covariance sampling. The stability analysis should be accurate to ensure the correctness of covariance sampling. In this paper, we go deep into the details of covariance sampling, and propose a few improvements for stability analysis. We theoretically and experimentally show that these improvements are necessary for further success in covariance sampling. Experimental results show that the proposed method is more efficient and robust for the ICP algorithm.

scholarly journals Estimating intrafraction tumor motion during fiducial-based liver stereotactic radiotherapy via an iterative closest point (ICP) algorithm

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Wu-zhou Li ◽  
Zhi-wen Liang ◽  
Yi Cao ◽  
Ting-ting Cao ◽  
Hong Quan ◽  
...  
Keyword(s):  
Stereotactic Radiotherapy ◽  
Imaging System ◽  
Liver Tumors ◽  
Three Dimensional ◽  
Rigid Motion ◽  
Iterative Closest Point ◽  
Tumor Motion ◽  
Tracking Accuracy ◽  
Icp Algorithm ◽  
Anterior Posterior

Abstract Background Tumor motion may compromise the accuracy of liver stereotactic radiotherapy. In order to carry out a precise planning, estimating liver tumor motion during radiotherapy has received a lot of attention. Previous approach may have difficult to deal with image data corrupted by noise. The iterative closest point (ICP) algorithm is widely used for estimating the rigid registration of three-dimensional point sets when these data were dense or corrupted. In the light of this, our study estimated the three-dimensional (3D) rigid motion of liver tumors during stereotactic liver radiotherapy using reconstructed 3D coordinates of fiducials based on the ICP algorithm. Methods Four hundred ninety-five pairs of orthogonal kilovoltage (KV) images from the CyberKnife stereo imaging system for 12 patients were used in this study. For each pair of images, the 3D coordinates of fiducial markers inside the liver were calculated via geometric derivations. The 3D coordinates were used to calculate the real-time translational and rotational motion of liver tumors around three axes via an ICP algorithm. The residual error was also investigated both with and without rotational correction. Results The translational shifts of liver tumors in left-right (LR), anterior-posterior (AP),and superior-inferior (SI) directions were 2.92 ± 1.98 mm, 5.54 ± 3.12 mm, and 16.22 ± 5.86 mm, respectively; the rotational angles in left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions were 3.95° ± 3.08°, 4.93° ± 2.90°, and 4.09° ± 1.99°, respectively. Rotational correction decreased 3D fiducial displacement from 1.19 ± 0.35 mm to 0.65 ± 0.24 mm (P<0.001). Conclusions The maximum translational movement occurred in the SI direction. Rotational correction decreased fiducial displacements and increased tumor tracking accuracy.

scholarly journals A simulated annealing-based algorithm for selecting balanced samples

2021 ◽  
Author(s):  
Roberto Benedetti ◽  
Maria Michela Dickson ◽  
Giuseppe Espa ◽  
Francesco Pantalone ◽  
Federica Piersimoni
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Sample Selection ◽  
Auxiliary Information ◽  
Real Data ◽  
Simulation Experiments ◽  
Balanced Sampling ◽  
Inclusion Probabilities ◽  
Random Method ◽  
Annealing Algorithms

AbstractBalanced sampling is a random method for sample selection, the use of which is preferable when auxiliary information is available for all units of a population. However, implementing balanced sampling can be a challenging task, and this is due in part to the computational efforts required and the necessity to respect balancing constraints and inclusion probabilities. In the present paper, a new algorithm for selecting balanced samples is proposed. This method is inspired by simulated annealing algorithms, as a balanced sample selection can be interpreted as an optimization problem. A set of simulation experiments and an example using real data shows the efficiency and the accuracy of the proposed 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.

scholarly journals Deploying GIS Services into the Edge: A Study from Performance Evaluation and Optimization Viewpoint

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jianbing Zhang ◽  
Bowen Ma ◽  
Jiwei Huang
Keyword(s):  
Optimization Problem ◽  
Real Life ◽  
Computer Software ◽  
Edge Computing ◽  
Simulation Experiments ◽  
Computing Architecture ◽  
Mobile Gis ◽  
Service Deployment ◽  
Quantitative Analyses ◽  
Critical Requirement

Geographic information system (GIS) is an integrated collection of computer software and data used to view and manage information about geographic places, analyze spatial relationships, and model spatial processes. With the growing popularity and wide application of GIS in reality, performance has become a critical requirement, especially for mobile GIS services. To attack this challenge, this paper tries to optimize the performance of GIS services by deploying them into edge computing architecture which is an emerging computational model that enables efficient offloading of service requests to edge servers for reducing the communication latency between end-users and GIS servers deployed in the cloud. Stochastic models for describing the dynamics of GIS services with edge computing architecture are presented, and their corresponding quantitative analyses of performance attributes are provided. Furthermore, an optimization problem is formulated for service deployment in such architecture, and a heuristic approach to obtain the near-optimal performance is designed. Simulation experiments based on real-life GIS performance data are conducted to validate the effectiveness of the approach presented in this paper.

WTG’s Gearbox Condition Assessment by Matching the Hilbert-Huang Spectrum of Vibration Signal

2013 ◽  
Vol 291-294 ◽  
pp. 522-526
Author(s):  
Hai Ning Pan ◽  
Ming Qin ◽  
Lei Pan
Keyword(s):  
Vibration Signal ◽  
Condition Assessment ◽  
Assessment Method ◽  
Iterative Closest Point ◽  
Intrinsic Mode Functions ◽  
Turbine Generator ◽  
Vibration Signals ◽  
Icp Algorithm ◽  
Wind Turbine Generator ◽  
Mode Decomposition

A gearbox condition assessment method for the Wind Turbine Generator (WTG) is proposed. Vibration signal’s Intrinsic Mode Functions (IMF) are decomposed by Empirical Mode Decomposition (EMD). Normalized Hilbert-Huang and Direct Quadrature (DQ) method are used to determine the instantaneous frequency. The HHS of vibration signals is plotted and then is shifted to match the pre-defined faulty gear condition by the Iterative Closest Point (ICP) algorithm to diagnose their similarities. The principle and effectiveness of the proposed method are illustrated by simulation, the fault types of gearbox can be identified by ICP algorithm effectively.

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