scholarly journals A Planar-Dimensions Machine Vision Measurement Method Based on Lens Distortion Correction

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Qiucheng Sun ◽  
Yueqian Hou ◽  
Qingchang Tan ◽  
Guannan Li
Keyword(s):  
Measurement Method ◽  
Imaging System ◽  
Distortion Correction ◽  
Geometric Distortion ◽  
Lens Distortion ◽  
Feature Points ◽  
Projection Model ◽  
Vision Measurement ◽  
Uniform Model ◽  
Optical Imaging System

Lens distortion practically presents in a real optical imaging system causing nonuniform geometric distortion in the images and gives rise to additional errors in the vision measurement. In this paper, a planar-dimensions vision measurement method is proposed by improving camera calibration, in which the lens distortion is corrected on the pixel plane of image. The method can be divided into three steps: firstly, the feature points, only in the small central region of the image, are used to get a more accurate perspective projection model; secondly, rather than defining a uniform model, the smoothing spline function is used to describe the lens distortion in the measurement region of image, and two correction functions can be obtained by fitting two deviation surfaces; finally, a measurement method for planar dimensions is proposed, in which accurate magnification factor of imaging system can be obtained by using the correction functions. The effectiveness of the method is demonstrated by applying the proposed method to the test of measuring shaft diameter. Experimental data prove that the accurate planar-dimensions measurements can be performed using the proposed method even if images are deformed by lens distortion.

A High-Precision Lens Distortion Correction Method for Vision Measurement

2021 ◽  
Author(s):  
Xiaoping Hu ◽  
Cong Zhou ◽  
Yu Kang ◽  
Xiao Zhou ◽  
Xingang Mou
Keyword(s):  
High Precision ◽  
Correction Method ◽  
Distortion Correction ◽  
Lens Distortion ◽  
Vision Measurement

scholarly journals Nonmetric Correction of Lens Distortion Based on Entropy Measure

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Tianfei Chen ◽  
Lijun Sun ◽  
Qiuwen Zhang ◽  
Xiang Wu ◽  
Defeng Wu
Keyword(s):  
Artificial Bee Colony ◽  
Noise Suppression ◽  
Vision System ◽  
Distortion Correction ◽  
Geometric Distortion ◽  
Entropy Measure ◽  
Lens Distortion ◽  
Bee Colony ◽  
Imaging Characteristics ◽  
Perspective Model

In the real vision system, lens always inevitably contains nonlinear distortion, which leads to geometric distortion of digital image, so it must be corrected. In this paper, a nonmetric correction algorithm for lens distortion based on entropy measure is proposed. The algorithm uses the imaging characteristics of the space line in the ideal perspective model, and the distortion entropy is defined to measure the degree of lens distortion. For distortion curves with different distribution, the calculation dimension of distortion entropy measure is uniform, which can reduce the influence of curve inhomogeneity. On this basis, the modified distortion entropy measure with normalized weight is put forward to enhance the capability of noise suppression, and the distortion correction performance of the traditional interior point optimization algorithm, basic artificial bee colony (ABC) algorithm, and Gbest-guided artificial bee colony (GABC) algorithm is compared and analyzed. The simulation experiments demonstrate that the correction performance of GABC to optimize the modified distortion entropy measure with normalized weight is best, and it has strong robustness to noise. Finally, the actual image distortion correction examples verify the effectiveness of the proposed algorithm.

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 Dual-mode optical imaging system for fluorescence image-guided surgery

2014 ◽  
Vol 39 (13) ◽  
pp. 3830 ◽  
Author(s):  
Nan Zhu ◽  
Suman Mondal ◽  
Shengkui Gao ◽  
Samuel Achilefua ◽  
Viktor Gruev ◽  
...  
Keyword(s):  
Optical Imaging ◽  
Imaging System ◽  
Image Guided Surgery ◽  
Dual Mode ◽  
Fluorescence Image ◽  
Guided Surgery ◽  
Image Guided ◽  
Optical Imaging System
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