Detection Algorithm for Crop Multi-Centerlines Based on Machine Vision

2008 ◽  
Vol 51 (3) ◽  
pp. 1089-1097 ◽  
Author(s):  
H. Zhang ◽  
B. Chen ◽  
L. Zhang
Keyword(s):  
Machine Vision ◽  
Detection Algorithm

A template-free machine vision-based crop row detection algorithm

2020 ◽  
Vol 22 (1) ◽  
pp. 124-153
Author(s):  
Saba Rabab ◽  
Pieter Badenhorst ◽  
Yi-Ping Phoebe Chen ◽  
Hans D. Daetwyler
Keyword(s):  
Machine Vision ◽  
Detection Algorithm ◽  
Template Free ◽  
Free Machine

Human Detection Algorithm Optimization in Machine Vision

2020 ◽  
Vol 57 (10) ◽  
pp. 101006
Author(s):  
何倩倩 He Qianqian ◽  
张荣芬 Zhang Rongfen ◽  
刘宇红 Liu Yuhong
Keyword(s):  
Machine Vision ◽  
Detection Algorithm ◽  
Human Detection ◽  
Algorithm Optimization

Machine-Vision Based Defect Detection Algorithm for Packaging Bags

2019 ◽  
Vol 56 (9) ◽  
pp. 091501 ◽  
Author(s):  
李丹 Li Dan ◽  
白国君 Bai Guojun ◽  
金媛媛 Jin Yuanyuan ◽  
童艳 Tong Yan
Keyword(s):  
Machine Vision ◽  
Defect Detection ◽  
Detection Algorithm

Research on the Automatic Test System of Bus Dashboard Function Based on Machine Vision

2014 ◽  
Vol 621 ◽  
pp. 378-384
Author(s):  
Rong Xing Guo ◽  
Jie Wang ◽  
Peng Ge Ma
Keyword(s):  
Machine Vision ◽  
Network Architecture ◽  
Detection Algorithm ◽  
Test System ◽  
Automatic Identification ◽  
Automatic Test ◽  
Still Images ◽  
Real Time Processing ◽  
Automatic Test System ◽  
Working Principle

This paper studies the automatic test system of bus dashboard EOL (end of line) based on machine vision. Based on machine vision theory, Identification and detection algorithm of panel signal indicator elements and tachometer pointer readings was studied combining single-frame still images and real-time processing of color video image, the automatic parallel detection of multiple dashboard was realized by distributed network architecture. This paper first describes the function requirements, the overall composition and working principle of automatic test system. Then, it proposes an automatic identification and detection algorithm of dashboard symbol sheets and pointer position. Finally, it shows the designing of automatic test software with a self-learning and auto-detection function, and describes the working process of the software. The tests prove that the system is capable of realizing fast and accurate auto-test of bus dashboard functions based on the non-contact of machine vision, which improves the overall efficiency of the bus dashboard line.

A Flexible Roll Forming on-Line Detection Algorithm Based on Machine Vision

2012 ◽  
Vol 457-458 ◽  
pp. 287-292 ◽  
Author(s):  
Xu Peng Li ◽  
Bo Qian ◽  
Qiang Li
Keyword(s):  
Control System ◽  
Machine Vision ◽  
Sheet Metal ◽  
Sheet Metal Forming ◽  
Metal Forming ◽  
Detection Algorithm ◽  
Roll Forming ◽  
Loop Control ◽  
Flexible Roll Forming ◽  
Flexible Roll

For the flexible roll forming of the control system, this paper introduces a method that machine vision bind to the flexible roll forming control system. The detection method for sheet metal forming is image acquisition, image processing and other means for the extraction of sheet metal forming in some time section contour curve. Compare extraction section contour curve with the moment theory section contour curve in the control system, get the deviation value and feedback to the control system that interpolation arithmetic to realize closed loop control.

Research on Boundary Detection for Turnout Rail Components Based on Machine Vision

2013 ◽  
Vol 303-306 ◽  
pp. 617-620 ◽  
Author(s):  
Yan Dou ◽  
Yu Qian Zheng
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Boundary Detection ◽  
Detection Algorithm ◽  
Visual Observation ◽  
Machine Vision System ◽  
Human Beings ◽  
Size Measurement ◽  
Energy Diagram ◽  
Binarization Method

Boundary detection is very important in the size measurement of the turnout rail components using machine vision. A new algorithm about boundary detection based on machine vision system is proposed. First an improved median filtering algorithm was used to noise reduction an image. Second Gabor operator energy diagram is generated. Then an elliptical-butterfly surround inhibition was designed to suppress the textures and enhance the boundary. Last a new binarization method and boundary detection algorithm is put forward according to the human beings visual observation. Experimental results show that the algorithm has good feasibility and effectiveness.

Research on Automatic Docking Location Method Based on Machine Vision

2013 ◽  
Vol 475-476 ◽  
pp. 405-408
Author(s):  
Gui Jun Yu ◽  
De Cai Lv
Keyword(s):  
Machine Vision ◽  
Edge Detection ◽  
Processing Speed ◽  
Detection Algorithm ◽  
Key Technology ◽  
Detection Technology ◽  
Key Factor ◽  
Automatic Docking ◽  
Sobel Edge Detection ◽  
Automatic Positioning

For many applications requiring automatic positioning docking, we often use machine vision to guide automatic docking location. The key technology of automatic positioning system based on machine vision is edge detection technology of docking interface; the algorithm is the key factor to decide the success or failure of the system. In this paper, the elliptic edge detection as an example, introduces the application and experiment using Sobel edge detection algorithm in positioning aspect. The experimental results show that the method has high precision for oval edge detection, can assist machine to carry out automatic docking location, and has shorter time of image processing, it is beneficial to improve the processing speed of docking.

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