• Proceedings of the Korea Multimedia Society Conference
• /
• 2012.05a
• /
• pp.26-27
• /
• 2012

Reflection considered as the view disturbing noise in optical systems, such as stereo camera in autonomous vehicles especially in night. Reflection caused by the street light or due to rainwater under adverse weather conditions. A blur image detected by the camera that results in wrong guidance to vehicle for detecting its track. A vehicle guidance approach through stereo vision can be same in day and night time. However it cannot be guided with same image analysis due to diverse illumination conditions. We develop the technique that shows its efficacy with illustrations of reflection removal off the camera lens and vehicle tracking control.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.20 no.1
• /
• pp.53-59
• /
• 2011

End milling is available for machining the various shape of products and has been widely applied in many manufacturing industries. The quality of products depends on a machine tool performance and machining conditions. Recognition characteristics of the cutting condition is becoming a critical requirement for improving the utilization and flexibility of present-day CNC machine tools. The measurement of tool wear would be performed by coordinate-measuring machine(CMM). However, the usage of CMM requires much time and cost. In order to overcome the difficulties, on-line measurement(OLM) system was applied for a tool wear measurement. This study shows a reliable technique for the reduction of machining error components by developing a system using a CCD camera and machine vision to be able to precisely measure the size of tool wear in flat end milling for CNC machining. The CCD camera and machine vision attached to a CNC machine can determine tool wear quickly and easily.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1994.10a
• /
• pp.119-124
• /
• 1994

Morden industrial society pursues unmanned system and automation of manufacturing rocess. Abreast with this tendensy, prodution of goods which requires advaned accuracy is increasing as well. According to this, the work sensing time of dressing by monitoring and diagnosis the condition of grinding, which is the representative way in accurate manufacturing, is a important work to prevent serios damages which affect grinding process or products by wearing wheel. Computer vision system is composed, so that grind wheel wurface was acquired by CCD camera and the change of cutting is composed. Then we used autometic threshoding technique from histogram as a way of deviding cutting edge which is used in manufacturing from the other parts. As a result, we are trying to approach unmanned system and sutomation by deciding more accurate time of dressing and by visualizing behavior of grinding wheel by marking use of computer vision.

• Journal of Biosystems Engineering
• /
• v.22 no.2
• /
• pp.217-226
• /
• 1997

This study was to develop a machine vision system to detect and to discriminate 5 kinds of apple surface defectbruise, decay. fleck, worm hole and scar. To detect the defects from an image of apple, thresholding technique was applied to images on various frames (R, G, B, H, S and I) of the color machine vision and an image of near infrared (NIR). To discriminate the detected region of defect, various features of the 5 kind defect regions were extracted from the 4 kinds of images selected above. The features were size of area, roundness, axes length ratio, mean and valiance of pixel values, standard deviation of real part of amplitude spectrum in frequency domain obtained by Fourier transform of pixel data and mean and standard deviation of power spectrum obtained by the same transform of pixel data. Routines to discriminate the defects from the features of image were developed and tested to prove their validity. The test resulted that I-frame and NIR images were the most desirable. Accuracies of the two images to discriminate the defects were noted as 76% and 77%, respectively.

• Journal of the Korean Society for Railway
• /
• v.12 no.3
• /
• pp.364-369
• /
• 2009

Pantograph sliding plate abrasion auto-detect system, one of the electric rail car auto-detecting devices, is a system that decides how much abrasion and when to replace without an inspector physically looking at the abrasion on the wet plate using machine vision, a cutting-edge technology. This paper covers the cause of deteriorating reliability that affects pantograph wet plate edge detection doe to noise added to the video when it rains. In order to remove such noise, problems should be checked through Smoothing, Averaging mask and Median filter using filtering technique and stable edge detection without being affected by noise should be induced in video measurement used in machine vision technology.

• Proceedings of the KIEE Conference
• /
• 1987.07a
• /
• pp.265-269
• /
• 1987

In an industrial vision system, a coding technique for binary image is essential to extract useful informations. To reduce the processing time, a hardware implementation of the chain coding algorithm is attemped. For that purpose, the chain coding algorithm is modified so that it is more suitable for a hardware implementation. A hardwired chain coder is also developed and tested with developed vision system. The result shows that the processing time is greatly reduced and that the developed vision system is maybe feasible for real-time applications.

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

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.10
• /
• pp.839-846
• /
• 2016

Bin picking is a task of picking a small object from a bin. For accurate bin picking, the 3D pose information, position, and orientation of a small object is required because the object is mixed with other objects of the same type in the bin. Using this 3D pose information, a robotic gripper can pick an object using exact distance and orientation measurements. In this paper, we propose a 3D vision technique for accurate measurement of 3D position and orientation of small objects, on which a paper label is stuck to the surface. We use a maximally stable extremal regions (MSERs) algorithm to detect the label areas in a left bin image acquired from a stereo camera. In each label area, image features are detected and their correlation with a right image is determined by a stereo vision technique. Then, the 3D position and orientation of the objects are measured accurately using a transformation from the camera coordinate system to the new label coordinate system. For stable measurement during a bin picking task, the pose information is filtered by averaging at fixed time intervals. Our experimental results indicate that the proposed technique yields pose accuracy between 0.4~0.5mm in positional measurements and $0.2-0.6^{\circ}$ in angle measurements.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.4
• /
• pp.27-36
• /
• 2022

In this paper, to improve the conditions of the fish processing site, we propose a method to predict the cutting point of fish according to the target weight using AI machine vision. The proposed method performs image-based preprocessing by first photographing the top and front views of the input fish. Then, RANSAC(RANdom SAmple Consensus) is used to extract the fish contour line, and then 3D external information of the fish is obtained using 3D modeling. Next, machine learning is performed on the extracted three-dimensional feature information and measured weight information to generate a neural network model. Subsequently, the fish is cut at the cutting point predicted by the proposed technique, and then the weight of the cut piece is measured. We compared the measured weight with the target weight and evaluated the performance using evaluation methods such as MAE(Mean Absolute Error) and MRE(Mean Relative Error). The obtained results indicate that an average error rate of less than 3% was achieved in comparison to the target weight. The proposed technique is expected to contribute greatly to the development of the fishery industry in the future by being linked to the automation system.

• 제어로봇시스템학회:학술대회논문집
• /
• 1995.10a
• /
• pp.295-298
• /
• 1995

In this paper, one simple technique to calibrate the system setting of the three-dimensional measuring system is presented. Due to this technique, the three-dimensional shape of the huge structures and the buildings can be readily obtained. This technique is applied to the three-dimensional landscape simulation. Two examples are shown in this paper.