• Journal of Biomedical Engineering Research
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• v.21 no.3 s.61
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• pp.321-331
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• 2000

Multiple 15-hue tests were designed and implemented on a PC in the study so as to quickly and quantitatively evaluate color vision acuity. Difficulty of the test was control)ed by the value of CDBACC (color difference between adjacent color chips) calculated using a CIELAB formula. The multiple 15-hue tests consist of eight of the hue tests (test 3-10) and three of the basic color (red, green, blue) tests (test 11-13). The 15 colors used for the hue tests were specified by the 15 color coordinates that were located at a constant distance (d = 2. 3. 5. 7, 10, 20, 30. 40) from white reference in the CIE chromaticity coordinate system and were separated by a constant color difference (CDBACC = 0.75, 1.1, 1.8. 2.5. 3.5. 7.5. 11, 14) from the adjacent chips. The color coordinates for the 15 chips for the basic color tests were the same as those of the 15 points spaced equally by a constant color difference (6.87 for the green color test. 7.27 for the red color test, 7.86 for the blue color test) from the white reference along the axis of red, green and blue. Thirty normal subjects who were not color blind were taken to undergo the multiple 15-hue tests. It was observed that most of the subjects correctly arranged color chips for the tests with CDBACC greater than 5, whereas no one correctly answered for those with CDBACC less than 2. Rapid changes in the number of the subjects correctly arranged took place when CDBACC of the tests was between 2 and 4.5. In the basic color tests, unlike the hue tests having similar values of CDBACC, it was seen that the subjects arranged color chips even less correctly. It was found that JNCD (just noticeable color difference) - a measure of color vision acuity was about 3 in average for the subjects. The JNCD was chosen as the value of the CDBACC of the test for which about $50\%$ of the subjects failed to successfully arrange color chips. ERCCA (error rate of color chips arrangement) for the test with CDBACC the same as the JNCD was shown to be about $20\%$. It is expected that the multi 15-hue tests implemented on a PC in the study will be an economical tool to quickly and quantitatively evaluate color vision acuity and, accordingly, the tests can be used for early diagnosis to massive potential patients suffering from diseases (ex. diabetes, glaucoma) which may induce changes in color vision acuity.

• Current Research on Agriculture and Life Sciences
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• v.30 no.2
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• pp.76-81
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• 2012

In this study the natural colorant was extracted from black bean seed coat in aqueous solution and used to dye silk and cotton fabrics. To obtain the optimal dyeing conditions it was examined at various dyeing condition (temperature, pH, time and liquor ratio). The dyeing behavior and the depths of shade which were evaluated in terms of K/S and CIELAB color difference values of the dyed and mordanted fabrics were also investigated. The dyeing fastness evaluated standard light and wash fastness tests. The obtained results were as follows ; The most K/S value of silk and cotton fabrics were obtained when the pH was 4.0 and 4.8, respectively and it increased slightly with dyeing time passes when the dyeing temperatures were at $40^{\circ}C$ and $60^{\circ}C$ but, it increased at $80^{\circ}C$. The color of silk fabrics changed from yellowish red to yellow at only Fe mordanting among various mordanting. Sn and Ti mordanting of silk fabric and only Ni mordanting of cotton fabric increased the $L^*$ values, but the others decreased. The light fastness of silk fabrics showed 4-6 grade without mordant, 4-5 grade with Al, 3-4 grade with Cu and Sn, and 2-3 grade with Fe as mordant, and that of cotton fabrics showed 1-2 grade without mordant, 2-3 grade with Fe, 2 grade with Cu, 1-2 grade with Al and Sn as mordants. All mordanting coluld not improve the light fastness of fabrics. Washing fastness(fade) of silk fabrics showed 2 grade without mordants and 2-3 grade with mordants and those of cotton fabrics showed 4 grade with Cu, 3 grade without mordant and with Al, Sn and Fe. All of the washing fastness(stain) of both fabrics showed 4-5 grade.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.109-125
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• 2019

As interest on intelligent search engines increases, various studies have been conducted to extract and utilize the features related to products intelligencely. In particular, when users search for goods in e-commerce search engines, the 'color' of a product is an important feature that describes the product. Therefore, it is necessary to deal with the synonyms of color terms in order to produce accurate results to user's color-related queries. Previous studies have suggested dictionary-based approach to process synonyms for color features. However, the dictionary-based approach has a limitation that it cannot handle unregistered color-related terms in user queries. In order to overcome the limitation of the conventional methods, this research proposes a model which extracts RGB values from an internet search engine in real time, and outputs similar color names based on designated color information. At first, a color term dictionary was constructed which includes color names and R, G, B values of each color from Korean color standard digital palette program and the Wikipedia color list for the basic color search. The dictionary has been made more robust by adding 138 color names converted from English color names to foreign words in Korean, and with corresponding RGB values. Therefore, the fininal color dictionary includes a total of 671 color names and corresponding RGB values. The method proposed in this research starts by searching for a specific color which a user searched for. Then, the presence of the searched color in the built-in color dictionary is checked. If there exists the color in the dictionary, the RGB values of the color in the dictioanry are used as reference values of the retrieved color. If the searched color does not exist in the dictionary, the top-5 Google image search results of the searched color are crawled and average RGB values are extracted in certain middle area of each image. To extract the RGB values in images, a variety of different ways was attempted since there are limits to simply obtain the average of the RGB values of the center area of images. As a result, clustering RGB values in image's certain area and making average value of the cluster with the highest density as the reference values showed the best performance. Based on the reference RGB values of the searched color, the RGB values of all the colors in the color dictionary constructed aforetime are compared. Then a color list is created with colors within the range of ${\pm}50$ for each R value, G value, and B value. Finally, using the Euclidean distance between the above results and the reference RGB values of the searched color, the color with the highest similarity from up to five colors becomes the final outcome. In order to evaluate the usefulness of the proposed method, we performed an experiment. In the experiment, 300 color names and corresponding color RGB values by the questionnaires were obtained. They are used to compare the RGB values obtained from four different methods including the proposed method. The average euclidean distance of CIE-Lab using our method was about 13.85, which showed a relatively low distance compared to 3088 for the case using synonym dictionary only and 30.38 for the case using the dictionary with Korean synonym website WordNet. The case which didn't use clustering method of the proposed method showed 13.88 of average euclidean distance, which implies the DBSCAN clustering of the proposed method can reduce the Euclidean distance. This research suggests a new color synonym processing method based on RGB values that combines the dictionary method with the real time synonym processing method for new color names. This method enables to get rid of the limit of the dictionary-based approach which is a conventional synonym processing method. This research can contribute to improve the intelligence of e-commerce search systems especially on the color searching feature.