• The Journal of Korean Medicine
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• v.35 no.2
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• pp.41-46
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• 2014

Objectives: We have treated people who have color vision defects with oriental medicine; here we demonstrate several representative cases to illustrate the effectiveness of our treatment. Material and methods: We recruited study patients who visited one oriental hospital for color vision defects. We show several representative cases to illustrate the effectiveness of our treatment. The study initially consisted of 178 subjects who took part during a 3-year period. Subjects, all of whom consented to this treatment and trial, were classified by those who have a color vision defect and those who don't by the Ishihara test and another by the Hahn color vision test. We tried color vision correction treatment with acupuncture. Acupuncture therapy where the retina and optic nerves were activated was conducted in parallel with vision correction. Assessment of therapy was conducted at 30 times, 60 times, 90 times, or 120 times of therapy. Results: Assessment of therapy was performed by conducting treatment 30 times, 60 times, 90 times, or 120 times. Surprisingly, all subjects could correctly recognize color after the treatment; although there were case by case differences according to the number of therapy treatments each individual received. Conclusions: Color vision defects can be treated. To enable those with color vision defects to enjoy better quality of life and increased opportunity in color vision-dependent job fields, therapy to correct the problem is a viable option.

• Journal of Korean Ophthalmic Optics Society
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• v.6 no.1
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• pp.81-85
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• 2001

The color vision was composed of the wavelength absorption of three R. G. B cone photo-receptors and the r-g, y-b channel of an opponent process. The color vision defects mechanism for the electric circuit made up a photo cell, relay switch and transformer. This mechanism very well applied to the color vision defects mechanism owing to be y-b chromatic valence function in case of a cone R or G defects and to be r-g chromatic valence function in case of a cone B defects.

• Journal of Korean Ophthalmic Optics Society
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• v.1 no.1
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• pp.127-138
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• 1996

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

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.852-862
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• 1996

This study was carried out to develop tools to detect defects of apple using machine vision. For the purpose, 6 kinds of frame for color images, R, G, B, h, S, and I frame, and a frame for near infra-red images (NIR frame) were tested first to select one which is useful to segment defect areas from apple images. After then, several methods to classify kind of defect for the segmented defect areas were developed and tested. Five kinds of apple defect -bruise , decay ,fleck worm hole and scar were investigated . The results are as follows: NIR frame was selected as the best one among the 7 kinds of image frame, and R, G and I frames showed favourable result to segment areas of apple defect. Various features of the segmented defect areas were measured to classify the defect areas. Eight kids of feature of the areas-size, roundness, axes length ratio, mean and variance of pixel values, variance of real part of spectrum, mean and variance of power spectrum resulted from spacial ourier transform were observed for the segmented defect areas in the selected 4 frames. then procedures to classify defects using the features were developed for the 4 frames and tested with 75-113 defects on apples. The test resulted that NIR and I frames showed high accuracies to classify the kind of defect as 77% and 76% , respectively.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.8
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• pp.761-771
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• 2009

Detecting defects on FPD (Flat Panel Display) color filter before the full panel is made is important to reduce the manufacturing cost. Among many types of defects, the low contrast blemish such as Suzi Mura is difficult to detect using standard CCD cameras. Even skilled inspectors in the inspection line can hardly identify such defects using bare eyes. To overcome this difficulty, point spectrometer has been used to analyze the spectrum to differentiate such defects from normal color filters. However, scanning ever increasing-size color filters by a point spectrometer takes too long time to be used in real production line. We propose a system using a spectral camera which can be viewed as a line scan camera composed of an array of point spectrometers. Three types of lighting system that exhibit different illumination spectrums are devised together with a calibration method of the proposed spectral camera system. To visualize the defect areas, various processing algorithms to identify and to enhance the small differences in spectrum between defective and normal areas are developed. Experiments shows 85% successful visualization. of real samples using the proposed system.

• Journal of Biosystems Engineering
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• v.26 no.2
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• pp.169-176
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• 2001

Defect of apple was depreciated the product value and causes storage disease seriously. To detect the defect of ‘Fuji’apple with machine vision system, the optical characteristics of defect should be investigated. In this research, absorbance spectra of defect were acquired by spectrophotometer in the range of visible and NIR region(400∼1,100nm) and L*a*b* color values were also acquired by colorimeter. NIR machine vision system was constructed with B&W camera, frame grabber, 16 tungsten-halogen lamps, variable focal length lens and NIR bandpass filter which was mounted to lens outward. Average gray values of defect at 15 NIR wavelength were acquired and the significant NIR wavelength was selected by comparing Mahalanobis distance between sound and defective apple. As the result of Mahalanobis distance analysis, the significant wavelength to discriminate the defectives in ‘Fuji’apple were found to be 720nm for scab and 970nm for bruise and cuts and 920nm was also effective regardless of defective types.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.463-465
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• 2021

Currently, the inspection method printed on food packages and boxes is to sample only a few products and inspect them with human eyes. Such a sampling inspection has the limitation that only a small number of products can be inspected. Therefore, accurate inspection using a camera is required. This paper proposes a deep learning object recognition technology model, which is an artificial intelligence technology, as a method for detecting the defects of expiration date printed on the product packaging. Using the Faster R-CNN (region convolution neural network) model, the color images, converted gray images, and converted binary images of the printed expiration date are trained and then tested, and each detection rates are compared. The detection performance of expiration date printed on the package by the proposed method showed the same detection performance as that of conventional vision-based inspection system.

• Journal of Biosystems Engineering
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• v.30 no.3 s.110
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• pp.172-178
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• 2005

Grading and sorting an indeterminate form of agricultural products such as sweet potatoes and potatoes are a labor intensive job because its shape and size are various and complicate. It costs a great deal to sort sweet potato in an indeterminate forms. There is a great need for an automatic grader fur the potatoes. Machine vision is the promising solution for this purpose. The optical indices for qualifying weight and appearance quality such as shape, color, defects, etc. were obtained and an on-line sorting system was developed. The results are summarized as follows. Sorting system combined with an on-line inspection device was composed of 5 sections, human inspection, feeding, illumination chamber, image processing & control, and grading & discharging. The algorithms to compute geometrical parameters related to the external guality were developed and implemented for sorting the deformed sweet potatoes. Grading accuracy by image processing was $96.4\%$ and the processing capacity was 10,800 pieces per hour.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.12
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• pp.38-44
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• 2019

Various automation studies have been conducted to detect defective products based on product images. In the case of machine vision-based studies, size and color error are detected through a preprocessing process. A situation may arise in which the main features are removed during the preprocessing process, thereby decreasing the accuracy. In addition, complex systems are required to detect various kinds of defects. In this study, we designed and developed a system to detect errors by analyzing various conditions of defective products. We designed the deep learning algorithm to detect the defective features from the product images during the automation process using a convolution neural network (CNN) and verified the performance by applying the algorithm to the checker-switch failure detection system. It was confirmed that all seven error characteristics were detected accurately, and it is expected that it will show excellent performance when applied to automation systems for error detection.