• 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.

• Science of Emotion and Sensibility
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• v.16 no.4
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• pp.557-566
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• 2013

This study is to investigate the color emotion associations of the color vision defectives, considering that the colors do have an effect on human emotional conditions. To realize this investigation, firstly we selected 100 normal persons (group C)and other 34 color vision defectives(group A), dividing the last group into two small groups as protanomaly group(group P) with 8 persons and deuteranomaly group(group D) with 16 persons. All participants have been offered to select one color from ten colors for each of three positive emotions such as 'favorite', 'happy' and 'friendly' and of three negative emotions like 'sad', 'disliked' and 'awkward'. And they selected another one color for each active and passive emotions. For 'favorite color' the group C selected 'blue' and 'red' while the group A chose 'blue'. For 'happy color' the two groups selected 'yellow'. For 'friendly color' the group C chose 'green', but the group A selected 'blue'. For 'sad color' the group C preferred 'blue', but the group A chose 'purple'. For 'disliked color' all groups selected 'bluish green'. For 'awkward color' the two groups preferred 'bluish green'. For 'active color' all groups selected 'red'. And for 'passive color' the group C chose 'bluish green', but the group A selected 'blue'. Depending on the type of color vision deficiency(group P and group D) some more differences were revealed relatively. These results should be applied to develop some intelligent color conversion technology for enhancing the usability of culture contents for color vision defectives.

• Journal of Preventive Medicine and Public Health
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• v.29 no.3 s.54
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• pp.529-538
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• 1996

We investigated the occurrence of color vision loss in 70 organic solvent mixtures exposed workers and in 47 controls. Color Vision was assessed with a color arrangement test designed to identify the defective color sense, the Han Double 15-Hue Test. The results of the test were no significant difference between exposed workers and controls in the proportion of subjects who committed one or two errors. Quantitative analysis, using color confusion index(CCI), showed no signicant difference between exposed workers and controls. A significant linear correlation was present between age and CCI in both exposed workers (CCI=0.0056age + 0.94; r=0.23; p<0.05) and controls(CCI=0.0066age + 0.86; r=0.33; p<0.05). Qualitative analysis of the patterns on the hue circle showed that the prevalence of acquired dyschromatopsia was 21% in both and no significant difference. Multiple regression analyses showed that age was significantly related to color vision loss. These results did not provide evidence of a relationship between organic solvents exposure and incidence of color vision loss. In field studies for monitor the people at risk of the acquired color vision loss involving low-dose organic solvents exposed workers, both quantitative and qualitative information should be considered.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.551-559
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• 2000

An integrated on-line inspection system was constructed with seven cameras, half mirrors to split images, 720 nm and 970 nm band pass filters, illumination chamber having several tungsten-halogen lamps, one main computer, one color frame grabber, two 4-channel multiplexors, and flat plate conveyer, etc., so that a total of seven images, that is, one color image from the top side of an apple and two B/W images from each side (top, right and left) could be captured and displayed on a computer monitor through the multiplexor. One of the two B/W images captured from each side is 720nm filter image and the other is 970nm. With this system an on-line grading software was developed to evaluate appearance quality. On-line test results to the Fuji apples that were manually fed on the conveyer showed that grading accuracies of the color, defective and shape were 95.3%, 86% and 91%, respectively. Grading time was 0.35 sec per apple on an average. Therefore, this on-line grading system could be used for inspection of the final products produced from an apple sorting system.

• 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.

• 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 Broadcast Engineering
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• v.21 no.3
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• pp.425-435
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• 2016

In this paper, we propose a classification model by analyzing raw material images recorded using a color CCD camera to automatically classify good and defective agricultural products such as rice, coffee, and green tea, and raw materials. The current classifying agricultural products mainly depends on visual selection by skilled laborers. However, classification ability may drop owing to repeated labor for a long period of time. To resolve the problems of existing human dependant commercial products, we propose a vision based automatic raw material classification combining mean shift clustering and stepwise region merging algorithm. In this paper, the image is divided into N cluster regions by applying the mean-shift clustering algorithm to the foreground map image. Second, the representative regions among the N cluster regions are selected and stepwise region-merging method is applied to integrate similar cluster regions by comparing both color and positional proximity to neighboring regions. The merged raw material objects thereby are expressed in a 2D color distribution of RG, GB, and BR. Third, a threshold is used to detect good and defective products based on color distribution ellipse for merged material objects. From the results of carrying out an experiment with diverse raw material images using the proposed method, less artificial manipulation by the user is required compared to existing clustering and commercial methods, and classification accuracy on raw materials is improved.