Retinal image mosaicing using the radial distortion correction model

2008 ◽  
Author(s):  
Sangyeol Lee ◽  
Michael D. Abràmoff ◽  
Joseph M. Reinhardt
Keyword(s):  
Retinal Image ◽  
Distortion Correction ◽  
Image Mosaicing ◽  
Correction Model ◽  
Radial 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.

General Distortion Correction Model of Omnidirectional Structured-Light Vision Measurement

2019 ◽  
Vol 39 (2) ◽  
pp. 0215003
Author(s):  
孙军华 Sun Junhua ◽  
刘畅 Liu Chang ◽  
张洲 Zhang Zhou
Keyword(s):  
Structured Light ◽  
Distortion Correction ◽  
Correction Model ◽  
Vision Measurement ◽  
Structured Light Vision

RATIONAL RADIAL DISTORTION MODELS OF CAMERA LENSES WITH ANALYTICAL SOLUTION FOR DISTORTION CORRECTION

2004 ◽  
Vol 01 (02) ◽  
pp. 135-147 ◽  
Author(s):  
LILI MA ◽  
YANGQUAN CHEN ◽  
KEVIN L. MOORE
Keyword(s):  
Analytical Solution ◽  
Polynomial Approximation ◽  
Distortion Correction ◽  
Camera Model ◽  
Distortion Model ◽  
Radial Distortion ◽  
New Class ◽  
Comparable Accuracy ◽  
The Common ◽  
Distortion Parameters

The common approach to radial distortion is by the means of polynomial approximation, which introduces distortion-specific parameters into the camera model and requires estimation of these distortion parameters. The task of estimating radial distortion is to find a radial distortion model that allows easy undistortion as well as satisfactory accuracy. This paper presents a new class of rational radial distortion models with easy analytical undistortion formulae. Experimental results are presented to show that with this class of rational radial distortion models, satisfactory and comparable accuracy can be achieved.

Camera lens radial distortion correction using two-view projective invariants

2011 ◽  
Vol 36 (24) ◽  
pp. 4734 ◽  
Author(s):  
Xin Du ◽  
Hongdong Li ◽  
Yunfang Zhu
Keyword(s):  
Distortion Correction ◽  
Projective Invariants ◽  
Camera Lens ◽  
Radial Distortion
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