Research on Machine Vision Image Mosaic Algorithm for Multi-workpiece Cutting Platform

2020 ◽  
pp. 101-111
Author(s):  
Long Xue ◽  
Yingyu Cao ◽  
Junfen Huang ◽  
Jiqiang Huang ◽  
Zhengyu Zhang ◽  
...  
Keyword(s):  
Machine Vision ◽  
Image Mosaic

A Fast and Precise Measuring Method for Large Workpiece Based on Machine Vision

2010 ◽  
Vol 139-141 ◽  
pp. 2076-2081 ◽  
Author(s):  
Hu Zhou ◽  
Jian Guo Yang
Keyword(s):  
Machine Vision ◽  
Template Matching ◽  
Vision System ◽  
Measuring Method ◽  
Search Space ◽  
Field Of View ◽  
Image Mosaic ◽  
Machine Vision System ◽  
Stop Criterion ◽  
Machine Parts

Image measurement based on machine vision is a promising method for the precise measurements of machine parts. As for a large sized workpiece that exceeds the FOV (field of view) of the camera, image mosaic must be performed to implement measurement. While traditional template matching algorithms will lead to long runtime consumption, a combination of algorithms with precise mechanical positioning function using motion control was proposed and applied in the machine vision system. The manufacturing and installing errors have been taken into account to improve the positioning accuracy. This method minimized the search space and thereby reduced the runtime substantially. In addition, a stop criterion of the computation for the algorithms was applied to save a considerable amount of time in the process. Experimental results show that the precision of the image mosaic meets the requirements while speeding up the process significantly.

A Mutual Information Based Image Measurement for Large Workpiece

2010 ◽  
Vol 44-47 ◽  
pp. 3204-3209 ◽  
Author(s):  
Hu Zhou ◽  
Jian Guo Yang
Keyword(s):  
Mutual Information ◽  
Machine Vision ◽  
Motion Control ◽  
Search Space ◽  
Image Mosaic ◽  
Image Measurement ◽  
Similarity Metric ◽  
Control Schemes ◽  
Speed Up ◽  
Machine Parts

Image measurement based on machine vision is a promising method for the precise measurements of machine parts. As for a large sized workpiece that exceeds the FOV (field of view) of the camera, image mosaic must be performed to implement measurement. The problematic behavior of image mosaic is the contradiction between the precision of mosaic and the complex of algorithm adopted. Mutual information as a similarity metric has been explained and introduced into the mosaic operation in this paper. To obtain a satisfactory mosaic effect and measuring precision, a machine vision based configuration that integrates the illumination setup and motion control schemes was built to ensure the accuracy. The precise motion control system was utilized to limit the search space and speed up the operation. Experimental results show that the precision of the image mosaic with mutual information as a similarity metric meets the requirements.

Machine Vision

2009 ◽  
Author(s):  
Wesley E. Snyder ◽  
Hairong Qi
Keyword(s):  
Machine Vision

Classroom Lighting Energy-Saving Control System Based on Machine Vision Technology

2018 ◽  
pp. 143-149 ◽  
Author(s):  
Ruijie CHENG
Keyword(s):  
Control System ◽  
Machine Vision ◽  
Energy Saving ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Lighting Control ◽  
Lighting Energy ◽  
Neural Network Algorithm ◽  
Energy Saving Control ◽  
Model Algorithm

In order to further improve the energy efficiency of classroom lighting, a classroom lighting energy saving control system based on machine vision technology is proposed. Firstly, according to the characteristics of machine vision design technology, a quantum image storage model algorithm is proposed, and the Back Propagation neural network algorithm is used to analyze the technology, and a multi­feedback model for energy­saving control of classroom lighting is constructed. Finally, the algorithm and lighting model are simulated. The test results show that the design of this paper can achieve the optimization of the classroom lighting control system, different number of signals can comprehensively control the light and dark degree of the classroom lights, reduce the waste of resources of classroom lighting, and achieve the purpose of energy saving and emission reduction. Technology is worth further popularizing in practice.

Machine Vision, Let's Go into the Street

1997 ◽  
Vol 117 (10) ◽  
pp. 1339-1344
Author(s):  
Katsuhiko Sakaue ◽  
Hiroyasu Koshimizu
Keyword(s):  
Machine Vision

Machine Vision Technologies to Connect with Humans

2005 ◽  
Vol 125 (11) ◽  
pp. 692-695
Author(s):  
Kazunori UMEDA ◽  
Yoshimitsu AOKI
Keyword(s):  
Machine Vision

Multifunctional stereoscopic machine vision system with multiple 3D outputs

2020 ◽  
Keyword(s):  
Machine Vision ◽  
International Symposium ◽  
Fast Track ◽  
Vision System ◽  
Machine Vision System ◽  
Stereoscopic Displays ◽  
Electronic Imaging

Fast track article for IS&T International Symposium on Electronic Imaging 2020: Stereoscopic Displays and Applications proceedings.

A Flying Gray Ball Multi-illuminant Image Dataset for Color Research

2020 ◽  
Vol 64 (5) ◽  
pp. 50411-1-50411-8
Author(s):  
Hoda Aghaei ◽  
Brian Funt
Keyword(s):  
Machine Vision ◽  
Spectral Reflectance ◽  
Color Constancy ◽  
Ground Truth ◽  
Practical Approach ◽  
Post Processing ◽  
Illumination Estimation ◽  
Processing Step ◽  
Image Dataset

Abstract For research in the field of illumination estimation and color constancy, there is a need for ground-truth measurement of the illumination color at many locations within multi-illuminant scenes. A practical approach to obtaining such ground-truth illumination data is presented here. The proposed method involves using a drone to carry a gray ball of known percent surface spectral reflectance throughout a scene while photographing it frequently during the flight using a calibrated camera. The captured images are then post-processed. In the post-processing step, machine vision techniques are used to detect the gray ball within each frame. The camera RGB of light reflected from the gray ball provides a measure of the illumination color at that location. In total, the dataset contains 30 scenes with 100 illumination measurements on average per scene. The dataset is available for download free of charge.

Effect of Field Setup on Accuracy of Traffic Data Collection Using Machine Vision Technology

2002 ◽  
Vol 1804 (1) ◽  
pp. 62-66 ◽  
Author(s):  
Sunita Nadella ◽  
Lloyd A. Herman
Keyword(s):  
Machine Vision ◽  
Focal Length ◽  
Reference Points ◽  
Specific Reference ◽  
Traffic Data ◽  
Video Traffic ◽  
Vertical Angle ◽  
Traffic Data Collection ◽  
Camera Position ◽  
Parameter Ranges

Video traffic data were collected in 24 combinations of four different camera position parameters. A machine vision processor was used to detect vehicle speeds and volumes from the videotapes. The machine vision results were then compared with the actual vehicle volumes and speeds to give the percentage errors in each case. The results of the study provide a procedure with which to establish camera position parameters with specific reference points to help machine vision users select suitable camera positions and develop appropriate measurement error expectations. The camera position parameters that were most likely to produce the least overall volume and speed errors, for the specific site and field setup with the parameter ranges used in this study, were the low height of approximately 7.6 m (25 ft), with an upstream orientation (traffic moving toward the camera), a 50-mm (midangle) focal length, and a 15° vertical angle.

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