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

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.

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

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

Efficient nonlinear geometric distortion correction for scanned laser pico projectors

2015 ◽  
Vol 51 (6) ◽  
pp. 471-473
Author(s):  
Joo Dong Yun ◽  
Jong Hyuk Park ◽  
Seungjoon Yang
Keyword(s):  
Distortion Correction ◽  
Geometric Distortion ◽  
Nonlinear Geometric

Geometric distortion correction for streak camera imaging

2019 ◽  
Vol 28 (03) ◽  
pp. 1
Author(s):  
Ailin Liu ◽  
Jinjin Zhang ◽  
Baoping Guo
Keyword(s):  
Streak Camera ◽  
Distortion Correction ◽  
Geometric Distortion ◽  
Camera Imaging

Unmanned Aerial Vehicle Based Agricultural Remote Sensing Multispectral Image Processing Methods

2014 ◽  
Vol 905 ◽  
pp. 585-588 ◽  
Author(s):  
Yu Zhao ◽  
Fan Feng Meng ◽  
Jiang Feng
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
High Spatial Resolution ◽  
Distortion Correction ◽  
Geometric Distortion ◽  
Image Process ◽  
Image Mosaic ◽  
Aerial Vehicle ◽  
Image Collection ◽  
Process Level

In order to provide more flexibility in remote sensing image collection, unmanned aerial vehicle has been used to kinds of agricultural productions. Images acquired from the UAV based RS system were very useful as a result of their high spatial resolution and low turn-around time. This paper discussed general methods to process the multispectral RS data at image process level. The distortion correction caused by sensor was introduced. The geometric distortion comprised sensor distortion and external distortion caused by external parameters. At last, the general image mosaic methods were discussed.

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