Image Acquisition and Processing Equipment for Machine Vision

1990 ◽  
Author(s):  
Donald E. McClure
Keyword(s):  
Machine Vision ◽  
Image Acquisition ◽  
Processing Equipment

Identification of the Hook on Investment Casting Shell Line Based on Machine Vision

2012 ◽  
Vol 220-223 ◽  
pp. 1356-1361
Author(s):  
Xi Jie Tian ◽  
Jing Yu ◽  
Chang Chun Li
Keyword(s):  
Machine Vision ◽  
Investment Casting ◽  
Hough Transform ◽  
Image Matching ◽  
Feature Matching ◽  
Image Acquisition ◽  
Target Range ◽  
Target Area ◽  
Matching Method ◽  
Vertical Projection

In this paper, the idea identify the hook on investment casting shell line based on machine vision has been proposed. According to the characteristic of the hook, we do the image acquisition and preprocessing, we adopt Hough transform to narrow the target range, and find the target area based on the method combining the level projection and vertical projection, use feature matching method SIFT to do the image matching. Finally, we get the space information of the target area of the hook.

Machine Vision Technology for Indoor Simulated Tree Image Acquisition and Recognition

2003 ◽  
Author(s):  
Haitao Xiang ◽  
Jiaqiang Zheng ◽  
Hongping Zhou
Keyword(s):  
Machine Vision ◽  
Image Acquisition ◽  
Tree Image

The experiment study of image acquisition system based on 3D machine vision

2011 ◽  
Author(s):  
Haiying Zhou ◽  
Zexin Xiao ◽  
Xuefei Zhang ◽  
Zhe Wei
Keyword(s):  
Machine Vision ◽  
Image Acquisition ◽  
Acquisition System ◽  
Experiment Study ◽  
Image Acquisition System

Study on Image Processing of Wheel Valve Hole Based on Machine Vision

2011 ◽  
Vol 63-64 ◽  
pp. 541-546 ◽  
Author(s):  
Chang Chun Li ◽  
Shi Feng Wang ◽  
Jing Yu ◽  
Hua Guan Liu
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Machine Vision ◽  
Edge Detection ◽  
Basic Principle ◽  
Image Acquisition ◽  
Wheel Speed ◽  
Hole Edge

This paper discusses the basic principle for automatic searching the wheel valve hole based on machine vision. Image acquisition and image processing have been done, and we analyzed the factors that impact the image quality of wheel valve hole. This paper argues that many parameters such as the wheel speed, painting color, the distance between the camera and the valve hole, edge detection operator, and they will affect the quality of the image acquisition and image processing of valve hole.

Study on the Positioning Error of Turntable Based on Machine Vision System

2014 ◽  
Vol 530-531 ◽  
pp. 467-471
Author(s):  
Fu Sheng Yu ◽  
Zhong Guo Sun ◽  
Sheng Jiang Yin ◽  
Teng Fei Li ◽  
Wei Kang Shi
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Image Acquisition ◽  
Vertical Direction ◽  
Calibration Method ◽  
Positioning Error ◽  
Positioning Errors ◽  
Data Processing Software ◽  
Accuracy Measure ◽  
Edge Detection Method

This paper developed a turntable positioning error measurement system based on machine vision. The system consists of image acquisition devices, the image acquisition card, computer and data processing software and other components. Among them, the image acquisition devices consisted of two digital CCD cameras and two microscope objectives. The image acquisition devices capture images of fixture fixed on the turntable in horizontal and vertical direction. Then, the collected images are processed by adopting the filtering method, binarization method, edge detection method, calibration method and other steps. The high-accuracy measure of turntables positioning errors is realized, and the error histogram is drawn. Theoretical analysis and experimental results show that the method is correct and feasible.

scholarly journals High-speed global shutter CMOS machine vision sensor with high dynamic range image acquisition and embedded intelligence

2012 ◽  
Author(s):  
Francisco Jiménez-Garrido ◽  
José Fernández-Pérez ◽  
Cayetana Utrera ◽  
José Ma. Muñoz ◽  
Ma. Dolores Pardo ◽  
...  
Keyword(s):  
Machine Vision ◽  
High Speed ◽  
Dynamic Range ◽  
Image Acquisition ◽  
High Dynamic Range ◽  
Range Image ◽  
Vision Sensor ◽  
High Dynamic Range Image ◽  
High Dynamic ◽  
Embedded Intelligence

scholarly journals Wear and Breakage Detection of Integral Spiral End Milling Cutters Based on Machine Vision

Materials ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 5690
Author(s):  
Wenming Wei ◽  
Jia Yin ◽  
Jun Zhang ◽  
Huijie Zhang ◽  
Zhuangzhuang Lu
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
End Milling ◽  
Image Acquisition ◽  
Acquisition System ◽  
Moving Frame ◽  
Light Sources ◽  
Complex Structures ◽  
Processing Methods ◽  
Image Acquisition System

Tool wear and breakage detection technologies are of vital importance for the development of automatic machining systems and improvement in machining quality and efficiency. The monitoring of integral spiral end milling cutters, however, has rarely been investigated due to their complex structures. In this paper, an image acquisition system and image processing methods are developed for the wear and breakage detection of milling cutters based on machine vision. The image acquisition system is composed of three light sources and two cameras mounted on a moving frame, which renders the system applicable in cutters of different dimensions and shapes. The images captured by the acquisition system are then preprocessed with denoising and contrast enhancing operations. The failure regions on the rake face, flank face and tool tip of the cutter are extracted with the Otsu thresholding method and the Markov Random Field image segmentation method afterwards. Eventually, the feasibility of the proposed image acquisition system and image processing methods is demonstrated through an experiment of titanium alloy machining. The proposed image acquisition system and image processing methods not only provide high quality detection of the integral spiral end milling cutter but can also be easily converted to detect other cutting systems with complex structures.

scholarly journals An Automatic Machine Vision-Guided System for the Propagation of Potato Test-Tube Plantlets

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xu Shengyong ◽  
Peng Biye ◽  
Wu Haiyang ◽  
Li Fushuai ◽  
Cai Xingkui ◽  
...  
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Vision System ◽  
Image Acquisition ◽  
Processing System ◽  
Test Tube ◽  
Automatic Machine ◽  
Node Point ◽  
Acquisition Device ◽  
Image Acquisition Device

In manually propagating potato test-tube plantlets (PTTPs), the plantlet is usually grasped and cut at the node point between the cotyledon and stem, which is hardly located and is easily damaged by the gripper. Using an agricultural intelligent robot to replace manual operation will greatly improve the efficiency and quality of the propagation of PTTPs. An automatic machine vision-guided system for the propagation of PTTPs was developed and tested. In this paper, the workflow of the visual system was designed and the image acquisition device was made. Furthermore, the image processing algorithm was then integrated with the image acquisition device in order to construct an automatic PTTP propagation vision system. An image processing system for locating a node point was employed to determine a suitable operation point on the stem. A binocular stereo vision algorithm was applied to compute the 3D coordinates of node points. Finally, the kinematics equation of the three-axis parallel manipulator was established, and the three-dimensional coordinates of the nodes were transformed into the corresponding parameters X, Y, and Z of the three driving sliders of the manipulator. The experimental results indicated that the automatic vision system had a success rate of 98.4%, 0.68 s time consumed per 3 plants, and approximate 1 mm location error in locating the plantlets in an appropriate position for the medial expansion period (22 days).

Transputer Based Parallel Processing Applied to Machine Vision Inspection

1991 ◽  
Author(s):  
Richard A. Carey ◽  
Wayne D. R. Daley ◽  
Jon S. Lindberg
Keyword(s):  
Feature Extraction ◽  
Machine Vision ◽  
Real Time ◽  
Image Enhancement ◽  
Image Acquisition ◽  
Image Interpretation ◽  
Real Time Processing ◽  
Flexible System ◽  
Vision Inspection ◽  
Machine Vision Inspection

Abstract The use of Machine vision systems has become more widespread in manufacturing processes for the purposes of quality control inspection, and product identification and sorting. Typical Machine Vision applications need to run in real time (30 frames per second), and as a result most of the existing systems are built from hardware to meet this speed requirement. There is currently no single processor that is reasonably priced and fast enough to provide real time performance on Machine Vision applications. This paper describes a Transputer based system that employs different architectures and algorithms to achieve real time processing speeds for some Machine Vision applications. The paper discusses the differences between sequential and parallel architectures, and the way the unique abilities of the Transputers are utilized to create a flexible system that provides the best performance for a variety of applications. The areas of Machine Vision discussed are Image Acquisition, Image Enhancement, Feature Extraction and Image Interpretation. Image Acquisition and interpretation are discussed briefly, with an in depth discussion of the algorithms and architecture needed to optimize Image Enhancement and Feature Extraction on a Transputer based system.

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