• 한국염색가공학회지
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• 제22권1호
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• pp.28-36
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• 2010

The deep black coloration of polyester fabrics was obtained by the physical properties of color and color mixing system. In this experiment, we have measured the absorbance and the reflectance of various disperse dyes for accomplishing the lowest lightness value and uniform reflectance, and new matching algorithm and computer color matching was made. The matching used both isomeric and metameric matching. The color matching of deep black color represented low lightness. Though actual reflectance of dyed polyester fabrics using these matching results was as high as theoretical one, low lightness value($L^*$) and uniform appearance were achieved.

• 한국군사과학기술학회지
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• 제23권2호
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• pp.139-146
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• 2020

The camouflage pattern was difficult to distinguish from the surrounding background, so it was difficult to classify the object and the background image when the color image is used as the training data of deep-learning. In this paper, we proposed a red-green color blindness image transformation method using the principle that people of red-green blindness distinguish green color better than ordinary people. Experimental results show that the camouflage soldier's recognition performance improved by proposed a deep learning model of the ensemble technique using the imitated red-green-blind image data and the original color image data.

• International journal of advanced smart convergence
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• 제8권2호
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• pp.116-125
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• 2019

The fire should extinguish as soon as possible because it causes economic loss and loses precious life. In this study, we propose a new atypical fire and smoke detection algorithm using deep learning and color histogram of fire and smoke. First, input frame images obtain from the ONVIF surveillance camera mounted in factory search motion candidate frame by motion detection algorithm and mean square error (MSE). Second deep learning (Faster R-CNN) is used to extract the fire and smoke candidate area of motion frame. Third, we apply a novel algorithm to detect the fire and smoke using color histogram algorithm with local area motion, similarity, and MSE. In this study, we developed a novel fire and smoke detection algorithm applied the local motion and color histogram method. Experimental results show that the surveillance camera with the proposed algorithm showed good fire and smoke detection results with very few false positives.

• Architectural research
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• 제18권3호
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• pp.83-90
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• 2016

Senescent effects are the gradual deterioration of function caused by biological aging. Senescent effects on color vision are not clearly understood even after considerable researches. Part of the reason is that the color vision is a complex phenomenon resulting from various factors such as organic systems, and the physical (neuro-optical) and the psychological (experiential) processes of color perception. We performed a field experiment on color perceptional differences due to aging vision. Our experiment was applied to two different groups in South Korea: an experimental group (46 subjects of over the age of 61 years) and a control group (49 subjects in their twenties). The experimental tools are comprised of (1) six gradual yellowing detector board (40%, 50%, 60%, 70%, 80%, 90%); (2) pairs of vivid-strong, vivid-deep, grayish-deep, deep-dull, and bright-light tones of Blue (B) and Purple (P) colors; (3) Red (R), Yellow (Y), Green (G), Blue (B), and Purple (P) colors of dull-tones and pale-tones; and (4) a questionnaire on the semantic differential scales of the color images and color differences. A diagnosis system of gradual yellow vision, developed by the authors for this study, was adapted to generate the color detecting boards. The results are as follows. (1) There are significant differences between the two groups in detecting colors that simulate 40% and 50% of yellow vision. (2) As to the color difference detecting ability between similar tones, the experimental group shows difficulties in pairs of vivid-strong tones and deep-dull tones of the B color. And (3), the emotional responses to the dull tone and the pale tone are not stable in the red, the yellow, blue, and purple. Thus, we empirically demonstrate the specific differences in color perception between the old and young groups.

• 한국염색가공학회지
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• 제15권4호
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• pp.1-7
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• 2003

We investigated the effect of color depth on polyester fabrics by plasma treatment. In this study, although it have many paper about effects of plasma treatment, we observed interfacial electrokinetic potential of polyester fabrics by plasma treatment and also we investigated relationship between deep coloring agent and plasma treatment to get the effect of color depth on polyester fabrics. The results obtained are as follows, 1. Plasma treatment did not enhanced the effect of color depth of polyester fabrics by plasma treatment independently. 2. In the case of using the deep coloring agent with plasma treatment on polyester fabrics, lightness was more decreased than using the deep coloring agent itself. 3. Plasma treatment could not affect surface shape and tensile strength of treated polyester fabrics.

• 반도체디스플레이기술학회지
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• 제23권2호
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• pp.23-27
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• 2024

Underwater images are typically degraded due to color distortion, light absorption, scattering, and noise from artificial light sources. Restoration of these images is an essential task in many underwater applications. In this paper, we propose a two-phase deep learning-based method, Underwater Deep Curve Estimation (UWDCE), designed to effectively enhance the quality of underwater images. The first phase involves a white balancing and color correction technique to compensate for color imbalances. The second phase introduces a novel deep learning model, UWDCE, to learn the mapping between the color-corrected image and its best-fitting curve parameter maps. The model operates iteratively, applying light-enhancement curves to achieve better contrast and maintain pixel values within a normalized range. The results demonstrate the effectiveness of our method, producing higher-quality images compared to state-of-the-art methods.

• 산업경영시스템학회지
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• 제46권3호
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• pp.59-68
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• 2023

Currently, yellow phosphor of Y3Al5O12:Ce3+ (YAG:Ce) fluorescent material is applied to a 450~480nm blue LED light source to implement a white LED device and it has a simple structure, can obtain sufficient luminance, and is economical. However, in this method, in terms of spectrum analysis, it is difficult to mass-produce white LEDs having the same color coordinates due to color separation cause by the wide wavelength gap between blue and yellow band. There is a disadvantage that it is difficult to control optical properties such as color stability and color rendering. In addition, this method does not emit purple light in the range of 380 to 420nm, so it is white without purple color that can not implement the spectrum of the entire visible light spectrum as like sunlight. Because of this, it is difficult to implement a color rendering index(CRI) of 90 or higher, and natural light characteristics such as sunlight can not be expected. For this, need for a method of implementing sunlight with one LED by using a method of combining phosphors with one light source, rather than a method of combining red, blue, and yellow LEDs. Using this method, the characteristics of an artificial sunlight LED device with a spectrum similar to that of sunlight were demonstrated by implementing LED devices of various color temperatures with high color rendering by injecting phosphors into a 405nm deep blue LED light source. In order to find the spectrum closest to sunlight, different combinations of phosphors were repeatedly fabricated and tested. In addition, reliability and mass productivity were verified through temperature and humidity tests and ink penetration tests.

• 한국의상디자인학회지
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• 제3권2호
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• pp.35-50
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• 2001

Cotton, Silk, Acrylic fabrics을 chitosan으로 처리하여 천연염료 자초로 Al, Sn, Cu등의 매염제를 이용한 염색을 하였더니 다음과 같은 결과가 나왔다. chitosan 처리포가 미처리포 보다 염색성이 모두 월등하게 우수했다. 또한 Al, Sn, Cu 등의 단독매염제의 사용시 보다 chiotosan과 함께 사용했을 때가 더욱 우수했다. 특히 아크릴포의 경우 무매염, Sn, Al 매염의 사용시 염색효과가 거의 발현되지 않았으나 chitosan 처리포의 경우 염색효과가 나타났으며, chiotosan 처리포를 중금속매염제와 함께 사용했을 때는 우수한 염색성이 발현되었다. 위 3종 중금속매염제 중 Cu로 chiotosan 처리포를 실험했을 때가 염색성이 가장 우수했다.

• 스마트미디어저널
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• 제11권3호
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• pp.74-89
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• 2022

섬유 소재의 염색은 기업별로 고유의 레시피와 공정으로 인하여 결과물 간의 차이가 존재할 뿐만 아니라 예측하기도 어려운 실정이다. 본 연구는 염색 공정에서의 색상구현 최적화를 위해 딥러닝 기반의 예측 모형을 개발하고자 시도되었다. 이를 위하여 딥러닝 기반 모형인 다층퍼셉트론, CNN 그리고 LSTM 모형을 선정하였다. 총 376건의 데이터 세트를 수집하여 3개의 예측 모형을 학습시켰다. 교차검증 방법을 이용하여 3개의 예측 모형에 대해 비교 및 분석하였다. LSTM 모형의 예측 결과에 대한 CMC(2:1) 색차의 평균이 가장 우수한 것으로 나타났다.

• Journal of Semiconductor Engineering
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• 제2권1호
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• pp.125-129
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• 2021

As the interest of deep learning, techniques to control the color of images in image processing field are evolving together. However, there is no clear standard for color, and it is not easy to find a way to represent only the color itself like the color-palette. In this paper, we propose a novel color palette extraction system by chroma fine-tuning with reinforcement learning. It helps to recognize the color combination to represent an input image. First, we use RGBY images to create feature maps by transferring the backbone network with well-trained model-weight which is verified at super resolution convolutional neural networks. Second, feature maps are trained to 3 fully connected layers for the color-palette generation with a generative adversarial network (GAN). Third, we use the reinforcement learning method which only changes chroma information of the GAN-output by slightly moving each Y component of YCbCr color gamut of pixel values up and down. The proposed method outperforms existing color palette extraction methods as given the accuracy of 0.9140.