• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1353-1369
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• 2023

In the event of a disaster accident at sea, the scale of damage will vary due to weather effects such as wind, currents, and tidal waves, and it is obligatory to minimize the scale of damage by establishing appropriate control plans through quick on-site identification. In particular, it is difficult to identify pollutants that exist in a thin film at sea surface due to their relatively low viscosity and surface tension among pollutants discharged into the sea. Therefore, this study aims to develop an algorithm to detect suspended pollutants on the sea surface in RGB images using imaging equipment that can be easily used in the field, and to evaluate the performance of the algorithm using input data obtained from actual waters. The developed algorithm uses image enhancement techniques to improve the contrast between the intensity values of pollutants and general sea surfaces, and through histogram analysis, the background threshold is found,suspended solids other than pollutants are removed, and finally pollutants are classified. In this study, a real sea test using substitute materials was performed to evaluate the performance of the developed algorithm, and most of the suspended marine pollutants were detected, but the false detection area occurred in places with strong waves. However, the detection results are about three times better than the detection method using a single threshold in the existing algorithm. Through the results of this R&D, it is expected to be useful for on-site control response activities by detecting suspended marine pollutants that were difficult to identify with the naked eye at existing sites.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.270-278
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• 2022

This study was conducted using deep learning technology to classify for 'Mihwang' peach maturity with RGB images and fruit quality attributes during fruit development and maturation periods. The 730 images of peach were used in the training data set and validation data set at a ratio of 8:2. The remains of 170 images were used to test the deep learning models. In this study, among the fruit quality attributes, firmness, Hue value, and a^* value were adapted to the index with maturity classification, such as immature, mature, and over mature fruit. This study used the CNN (Convolutional Neural Networks) models for image classification; VGG16 and InceptionV3 of GoogLeNet. The performance results show 87.1% and 83.6% with Hue left value in VGG16 and InceptionV3, respectively. In contrast, the performance results show 72.2% and 76.9% with firmness in VGG16 and InceptionV3, respectively. The loss rate shows 54.3% and 62.1% with firmness in VGG16 and InceptionV3, respectively. It considers increasing for adapting a field utilization with firmness index in peach.

• Journal of Fashion Business
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• v.14 no.1
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• pp.86-105
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• 2010

Vintage fashion is a lot influenced by colors because an emotion is transmitted via images of "old feeling", "worn-out feeling" and "faded feeling" etc. Colors are visual sensation occurring at a time when lights stimulate an eye, which is a representative factor which humans first perceive when they touch objects. And colors in clothing function as a critical element which indicates an individual's impression and character as well as aesthetic sensation. In this study, I examined on the theoretical consideration and aesthetic characteristics via the previous literature on vintage fashion and colors. As an empirical study, I investigated on the colors of vintage fashion appearing in Pr$\hat{e}$t-$\grave{a}$-porter Collections, Paris from Spring/Summer, 2003 to Fall/Winter 2008. As a way for study, I investigated into the total 197 vintage fashion photos and calculated their RGB values by using photoshop. And I converted the values of the colors extracted into H V/C values by using Munsell Conversion Version 9.0.6 and analyzed on Munsell System of 10 Color Notation and the PCCS colors, classifying a color scheme by visual sensation measurement. The result of analyzing on the concept of vintage fashion and its color characteristics is as follows; Vintage fashion made an appearance the most in 2003 and 2004 and its colors appeared a lot in Y, YR, R and PB lines. Color tone concentrated on black and white, achromatic color and low chroma colors in a grayish line, chromatic color. Thus, the study found that colors suitable for a "worn-out", "faded" and "old" image are properly reflected in vintage fashion rather than a clear and bright background. In a color scheme, I found contrast color and same color appearing a lot, which gave an unharmonious feeling and a smack of the country. The study reveals that the color characteristic of vintage fashion is relatively diverse and complex in color, color tone or shade and color scheme, which shows a color trend which reflects a non-constructive and complex coordination characteristic instead of a standardized simple and clear image.