Mr geometric distortion correction for improved frame-based stereotaxic target localization accuracy

1995 ◽  
Vol 34 (1) ◽  
pp. 106-113 ◽  
Author(s):  
Thilaka Sumanaweera ◽  
Gary H. Glover ◽  
Paul F. Hemler ◽  
Petra A. Den Van Elsen ◽  
David Martin ◽  
...  
Keyword(s):  
Distortion Correction ◽  
Target Localization ◽  
Geometric Distortion ◽  
Localization Accuracy

Evaluation of patient‐specific MR distortion correction schemes for improved target localization accuracy in SRS

2020 ◽  
Author(s):  
Dimitrios Dellios ◽  
Eleftherios P. Pappas ◽  
Ioannis Seimenis ◽  
Chryssa Paraskevopoulou ◽  
Kostas I. Lampropoulos ◽  
...  
Keyword(s):  
Distortion Correction ◽  
Target Localization ◽  
Patient Specific ◽  
Localization Accuracy

scholarly journals A Tree Ring Measurement Method Based on Error Correction in Digital Image of Stem Analysis Disk

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 464
Author(s):  
Wenjie Zhang ◽  
Tianzhong Zhao ◽  
Xiaohui Su ◽  
Baoguo Wu ◽  
Zhiqiang Min ◽  
...  
Keyword(s):  
Error Correction ◽  
Tree Ring ◽  
Tree Rings ◽  
Digital Image ◽  
Regression Equation ◽  
Distortion Correction ◽  
Geometric Distortion ◽  
Stem Analysis ◽  
Correction Model ◽  
Ring Radius

Stem analysis is an essential aspect in forestry investigation and forest management, as it is a primary method to study the growth law of trees. Stem analysis requires measuring the width and number of tree rings to ensure the accurate measurement, expand applicable tree species, and reduce operation cost. This study explores the use of Open Source Computer Vision Library (Open CV) to measure the ring radius of analytic wood disk digital images, and establish a regression equation of ring radius based on image geometric distortion correction. Here, a digital camera was used to photograph the stem disks’ tree rings to obtain digital images. The images were preprocessed with Open CV to measure the disk’s annual ring radius. The error correction model based on the least-square polynomial fitting method was established for digital image geometric distortion correction. Finally, a regression equation for tree ring radius based on the error correction model was established. Through the above steps, click the intersection point between the radius line and each ring to get the pixel distance from the ring to the pith, then the size of ring radius can be calculated by the regression equation of ring radius. The study’s method was used to measure the digital image of the Chinese fir stem disk and compare it with the actual value. The results showed that the maximum error of this method was 0.15 cm, the average error was 0.04 cm, and the average detection accuracy reached 99.34%, which met the requirements for measuring the tree ring radius by stem disk analysis. This method is simple, accurate, and suitable for coniferous and broad-leaved species, which allows researchers to analyze tree ring radius measurement, and is of great significance for analyzing the tree growth process.

Method of Optimization for Target Localization Model Parameters Based on LSSVR

2011 ◽  
Vol 268-270 ◽  
pp. 934-939
Author(s):  
Xue Wen He ◽  
Gui Xiong Liu ◽  
Hai Bing Zhu ◽  
Xiao Ping Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Parameters Optimization ◽  
Target Localization ◽  
Likelihood Method ◽  
Support Vector ◽  
Swarm Optimization ◽  
Localization Accuracy ◽  
Localization Method

Aiming at improving localization accuracy in Wireless Sensor Networks (WSN) based on Least Square Support Vector Regression (LSSVR), making LSSVR localization method more practicable, the mechanism of effects of the kernel function for target localization based on LSSVR is discussed based on the mathematical solution process of LSSVR localization method. A novel method of modeling parameters optimization for LSSVR model using particle swarm optimization is proposed. Construction method of fitness function for modeling parameters optimization is researched. In addition, the characteristics of particle swarm parameters optimization are analyzed. The computational complexity of parameters optimization is taken into consideration comprehensively. Experiments of target localization based on CC2430 show that localization accuracy using LSSVR method with modeling parameters optimization increased by 23%~36% in compare with the maximum likelihood method(MLE) and the localization error is close to the minimum with different LSSVR modeling parameters. Experimental results show that adapting a reasonable fitness function for modeling parameters optimization using particle swarm optimization could enhance the anti-noise ability significantly and improve the LSSVR localization performance.

scholarly journals Fast geometric distortion correction using a deep neural network: Implementation for the 1 Tesla MRI‐Linac system

2020 ◽  
Vol 47 (9) ◽  
pp. 4303-4315
Author(s):  
Mao Li ◽  
Shanshan Shan ◽  
Shekhar S. Chandra ◽  
Feng Liu ◽  
Stuart Crozier
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Distortion Correction ◽  
Geometric Distortion

Geometric distortion correction in prostate diffusion‐weighted MRI and its effect on quantitative apparent diffusion coefficient analysis

2017 ◽  
Vol 79 (5) ◽  
pp. 2524-2532 ◽  
Author(s):  
Gabriel Nketiah ◽  
Kirsten M Selnæs ◽  
Elise Sandsmark ◽  
Jose R. Teruel ◽  
Brage Krüger‐Stokke ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Distortion Correction ◽  
Geometric Distortion ◽  
Diffusion Weighted Mri ◽  
Diffusion Weighted ◽  
Apparent Diffusion

EPI Nyquist ghost and geometric distortion correction by two‐frame phase labeling

2016 ◽  
Vol 77 (5) ◽  
pp. 1749-1761 ◽  
Author(s):  
Victor B. Xie ◽  
Mengye Lyu ◽  
Ed X. Wu
Keyword(s):  
Distortion Correction ◽  
Geometric Distortion

Impact analysis of attitude determination accuracy to target localization accuracy on Multi-BD receivers

2016 ◽  
Vol 31 (9) ◽  
pp. 902-912
Author(s):  
蔡明兵 CAI Ming-bing ◽  
王超 WANG Chao ◽  
刘晶红 LIU Jing-hong ◽  
周前飞 ZHOU Qian-fei ◽  
宋悦铭 SONG Yue-ming
Keyword(s):  
Impact Analysis ◽  
Attitude Determination ◽  
Target Localization ◽  
Localization Accuracy

Geometric distortion correction for structured-light image of deep-hole inner-surface

2018 ◽  
Vol 26 (10) ◽  
pp. 2555-2564
Author(s):  
丁 超 DING Chao ◽  
唐力伟 TANG Li-wei ◽  
曹立军 CAO Li-jun ◽  
邵新杰 SHAO Xin-jie ◽  
邓士杰 DENG Shi-jie
Keyword(s):  
Structured Light ◽  
Distortion Correction ◽  
Geometric Distortion ◽  
Deep Hole ◽  
Light Image ◽  
Inner Surface
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