• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.270-278
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• 2000

A machine vision system using charge coupled device(CCD) camera for the weed detection in a radish farm was developed. Shape features were analyzed with the binary images obtained from color images of radish and weeds. Aspect, Elongation and PTB were selected as significant variables for discriminant models using the STEPDISC option. The selected variables were used in the DISCRIM procedure to compute a discriminant function for classifying images into one of the two classes. Using discriminant analysis, the successful recognition rate was 92% for radish and 98% for weeds. To recognize radish and weeds more effectively than the discriminant analysis, an artificial neural network(ANN) was used. The developed ANN model distinguished the radish from the weeds with 100%. The performance of ANNs was improved to prevent overfitting and to generalize well using a regularization method. The successful recognition rate in the farms was 93.3% for radish and 93.8% for weeds. As a whole, the machine vision system using CCD camera with the artificial neural network was useful to detect weeds in the radish farms.

• Journal of Biosystems Engineering
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• v.25 no.5
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• pp.409-414
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• 2000

A machine vision system was built to obtain single stationary image from an egg. This system includes a CCD camera, an image processing board and a lighting system. A computer program was written to acquire, enhance and get histogram from an image. To minimize the evaluation time, the artificial neural network with the histogram of the image was used for eggshell evaluation. Various artificial neural networks with different parameters were trained and tested. The best network(64-50-1 and 128-10-1) showed an accuracy of 87.5% in evaluating eggshell. The comparison test for the elapsed processing time per an egg spent by this method(image processing and artificial neural network) and by the processing time per an egg spent by this method(image processing and artificial neural network) and by the previous method(image processing only) revealed that it was reduced to about a half(5.5s from 10.6s) in case of cracked eggs and was reduced to about one-fifth(5.5s from 21.1s) in case of normal eggs. This indicates that a fast eggshell evaluation system can be developed by using machine vision and artificial neural network.

• Journal of Biosystems Engineering
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• v.32 no.4
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• pp.256-262
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• 2007

Image processing systems have been used to measure the plant parameters such as size, shape and structure of plants. There are yet some limited applications for evaluating plant colors due to illumination conditions. This study was focused to present adaptive methods to analyze plant leaf color regardless of illumination conditions. Color patches attached on the calibration bars were selected to represent leaf colors of lettuces and to test a possibility of health monitoring of lettuces. Repeatability of assigning leaf colors to color patches was investigated by two-tailed t-test for paired comparison. It resulted that there were no differences of assignment histogram between two images of one lettuce that were acquired at different light conditions. It supported that use of the calibration bars proposed for leaf color analysis provided color constancy, which was one of the most important issues in a video color analysis. A health discrimination equation was developed to classify lettuces into one of two classes, SOUND group and POOR group, using the machine vision. The classification accuracy of the developed health discrimination equation was 80.8%, compared to farmers' decision. This study could provide a feasible method to develop a standard color chart for evaluating leaf colors of plants and plant health monitoring system using the machine vision.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.11
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• pp.4145-4150
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• 2010

This research aims to design on an automatic machine vision system to replace eye inspection of alternator spool which is one of the key automotive parts. The alternator spool, plastic extrusion part would have various defects like unfinished, crack and burr. Through the design failure examples the optimized fast machine vision system will be designed to inspect all spools also focuses on the low cost machine for the middle sized company as 2'nd automotive supplier. 3-dimensional design softwares of Pro-Engineer & CATIA were used and the system were built based on the design. The system will contribute to satisfy the cycle time and can inspect each part in an absolutely accurate method, which is sufficient for industrial applications.

• Journal of the Semiconductor & Display Technology
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• v.16 no.3
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• pp.8-13
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• 2017

In this paper, the measurement of the inner diameter dimension of the circular hole by using a machine vision system was studied. This paper was focused on the theory and key technologies of machine vision inspection technology for the improvement of measurement accuracy and speed of the micro circular holes. A new method was proposed and was verified through the experiments on Gray conversion, binarization, edge extraction and Hough transform in machine vision system processes. Firstly, the Hough transform was proposed in order to improve the speed increase and implementation ease, it demonstrated the superiority of Hough transform and improvement through a comparative experiment. Secondly, we propose a calibration method of the system in order to obtain exactly the inner diameter of the circular hole. Finally, we demonstrate the reliability of the entire system as a MATLAB-based implementation of the GUI program, measuring the inner diameter of the circular hole through the circular holes of different dimensions measuring experiment.

• Transactions of Materials Processing
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• v.32 no.1
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• pp.20-27
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• 2023

In this study, the deformation of materials was measured using the video and tracking API of OpenCV. Circular markers attached to the material were selected the region of interests (ROIs). The position of the marker was measured from the area center of the circular marker. The position and displacement of the center point was measured along the image frames. For the verification, tensile tests were conducted. In the tensile test, four circular markers were attached along the longitudinal and transverse directions. The strain was calculated using the distance between markers both in the longitudinal and transverse direction. As a result, the stress-strain curve obtained using machine vision is compared to the stress-strain curve obtained from the DIC results. RMSE values of the strain from the machine vision and DIC were less than 0.005. In addition, as a measurement example, a bending angle and springback measurement according to bending deformation, and a moving position measurement of a punch, a blank holder, and a die by time change were performed. Using the proposed method, the deformation and displacement of the materials were measured precisely and easily.

• Journal of Mechanical Science and Technology
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• v.18 no.7
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• pp.1055-1061
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• 2004

In this research, a vision system for detecting breakages of small-diameter taps, which are rarely detected by the indirect in-process monitoring methods such as acoustic emission, cutting torque and motor current, was developed. Two HMI (Human Machine Interface) programs to embed the developed vision system into a Siemens open architecture controller, 840D, were developed. They are placed in sub-windows of the main window of the 840D and can be activated or deactivated either by a softkey on the operating panel or the M code in the NC part program. In the event that any type of tool breakage is detected, the HMI program issues a command for an automatic tool change or sends an alarm signal to the NC kernel. An evaluation test in a high-speed tapping machine showed that the developed vision system was successful in detecting breakages of small-diameter taps up to M1.

• Journal of Biosystems Engineering
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• v.32 no.3
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• pp.179-189
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• 2007

Most of the seeds of cucurbitaceous rootstock species used for grafting were mainly sown by hand. This study was carried out to develop an on-line discriminating algorithm of seed direction using machine vision and an automatic seeding system. The seeding system was composed of a supplying device, feeding device, machine vision system, reversing device, seeding device and system control section. Machine vision was composed of a color CCD camera, frame grabber, image inspection chamber, lighting and personal computer. The seed image was segmented into a region of seed part and background part using thresholding technique in which H value of HSI color coordinate system. A seed direction was discriminated by comparing position between the center of circumscribed rectangle to a seed and the center of seed image. It took about 49ms to identify and redirect seed. Line-up status of seed was good the more than 95% of a sowed seed. Seeding capacity of this system was shown to be 10,140 grains per hour, which is three times faster than that of a typical worker.

• Journal of Practical Engineering Education
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• v.6 no.1
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• pp.37-42
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• 2014

The automated visual inspection system (machine vision system) for quality assurance is important factory automation equipment in the manufacturing industries, such as display, semiconductor, etc. There is a lot of demand for the machine vision engineers. However, there are no technical training courses for machine vision technologies in vocational schools, colleges and universities. In this paper, we present the implementation of line scan camera based equipment for technical training of the automated visual inspection system. The training system consists of the X-Y stage which is widely used in machine vision industries and its variable image resolution are set to $10-30{\mu}m$. Additionally, this training system can attach the industrial illumination, either the direct illuminator or coaxial illuminator, for verifying the effect of illuminations. This means that the trainee can have a practical training in various equipment conditions and the training system is similar to the automated visual inspection system in industries.

• Journal of Biosystems Engineering
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• v.42 no.3
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• pp.227-233
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• 2017

Purpose: Morphological properties of soybean roots are important indicators of the vigor of the seed, which determines the survival rate of the seedlings grown. The current vigor test for soybean seeds is manual measurement with the human eye. This study describes an application of a machine vision technique for rapid measurement of soybean seed vigor to replace the time-consuming and labor-intensive conventional method. Methods: A CCD camera was used to obtain color images of seeds during germination. Image processing techniques were used to obtain root segmentation. The various morphological parameters, such as primary root length, total root length, total surface area, average diameter, and branching points of roots were calculated from a root skeleton image using a customized pixel-based image processing algorithm. Results: The measurement accuracy of the machine vision system ranged from 92.6% to 98.8%, with accuracies of 96.2% for primary root length and 96.4% for total root length, compared to manual measurement. The correlation coefficient for each measurement was 0.999 with a standard error of prediction of 1.16 mm for primary root length and 0.97 mm for total root length. Conclusions: The developed machine vision system showed good performance for the morphological measurement of soybean roots. This image analysis algorithm, combined with a simple color camera, can be used as an alternative to the conventional seed vigor test method.