• Textile Coloration and Finishing
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• v.22 no.1
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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.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.2
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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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• v.8 no.2
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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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• v.18 no.3
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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.

• Textile Coloration and Finishing
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• v.15 no.4
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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.

• Journal of the Semiconductor & Display Technology
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• v.23 no.2
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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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.

• Journal of the Korea Fashion and Costume Design Association
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• v.3 no.2
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• pp.35-50
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• 2001

The object of this study is to improve the adsorption of dye for gromwell. Dye was from gromwell first soaked in methylol and added the distilled water, using same amount of methylol. The fabrics used for the experiments were cotton, silk and acrylics(KS0905). These were used untreated and pretreated with chitosan, premordanted with Cu, Al and Fe. Dyeing conditions were controlled. 1. Deep color effect was shown silk. 2. Chitosan treated cotton and acrylics showed deep color effect and huge color difference before and after the experiment. 3. In chitosan treated acrylics, deep color effect were shown. It proved the good adsorption of gromwel under metal mordanting. 4. Cu showed high adsorption of gromwell and deep color effect. 5. Chitosan treated acrylics can be substitute for wool.

• Smart Media Journal
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• v.11 no.3
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• pp.74-89
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• 2022

Due to the unique recipes and processes of each company, not only differences among the results of dyeing textile materials exist but they are also difficult to predict. This study attempted to develop a color prediction model based on deep learning to optimize color realization in the dyeing process. For this purpose, deep learning-based models such as multilayer perceptron, CNN and LSTM models were selected. Three forecasting models were trained by collecting a total of 376 data sets. The three predictive models were compared and analyzed using the cross-validation method. The mean of the CMC (2:1) color difference for the prediction results of the LSTM model was found to be the best.

• Journal of Semiconductor Engineering
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• v.2 no.1
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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.