• Korean Journal of Fisheries and Aquatic Sciences
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• v.56 no.6
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• pp.923-929
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• 2023

This study investigated the optical characterization of water types based on Jerlov's classification, employing the CIE colorimetric system. Digital visualization techniques were applied to articulate watercolor manifestations intuitively. The L^* luminance parameter exhibited a discernible reduction from optical water type I III and from type 1 to 9, registering a range between 66 and 84. Analysis of color attributes in each optical water type revealed that in the transition from type I to III, the color a^* values spanned from -7.43 to -8.32, while color b^* values ranged from -2.97 to -3.33. a^* values for optical water types 1 to 9 varied between -6.28 and -10.50, with corresponding b^* values ranging from -2.51 to -4.20. Consequently, optical water type I, IA, IB, II, and III were discretely categorized by independent color values, as were optical water types 1, 3, 5, 7, and 9. The digitized representation of watercolor in this inquiry facilitated comprehensive information asso,o;atopm. The study highlights limitations in Jerlov's classification for representing watercolors in different ocean conditions. It emphasized the need to collect color data from various marine areas and formulate a novel color standard or method for comparing colors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9C
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• pp.930-938
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• 2005

We propose a content-based image retrieval(CBIR) method based on an efncient combination of a color feature and multiresolution texture features. As a color feature, a HSV autocorrelograrn is chosen which is blown to measure spatial correlation of colors well. As texture features, BDIP and BVLC moments are chosen which is hewn to measure local intensity variations well and measure local texture smoothness well, respectively. The texture features are obtained in a wavelet pyramid of the luminance component of a color image. The extracted features are combined for efficient similarity computation by the normalization depending on their dimensions and standard deviation vectors. Experimental results show that the proposed method yielded average $8\%\;and\;11\%$ better performance in precision vs. recall than the method using BDIPBVLC moments and the method using color autocorrelograrn, respectively and yielded at least $10\%$ better performance than the methods using wavelet moments, CSD, color histogram. Specially, the proposed method shows an excellent performance over the other methods in image DBs contained images of various resolutions.

• Korean Journal of Optics and Photonics
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• v.15 no.2
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• pp.183-189
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• 2004

As high quality digital cameras become readily available, digital cameras are being used not only for simple picture recording but also as information storing media in various fields. However, due to the fact that the spectral responses of the camera sensors are different from color matching functions of the CIE standard observer, the color can not be measured using these cameras. This study shows a method for converting camera image to sRGB image, in which color information is preserved. The transfer matrix between camera output signals and CIE stimulus values was determined using a multiple regression method with Macbeth ColorChecker as target colors. The CIE stimulus values for camera output signals can be mapped with a transfer matrix, and these values are converted to sRGB signals. As the result of testing a Kodak DC220 digital camera, the average color difference of Macbeth ColorChecker between true and displayed colors was 2.1 $\Delta$ $E_{ab}$ $^{*}$.$^{*}$.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.21-28
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• 2009

Color polarity classification is a process to determine whether the color of text is bright or dark and it is prerequisite task for text extraction. In this paper we propose a color polarity method to extract text region. Based on the observation for the text and background regions, the proposed method uses the ratios of sizes and standard deviations of bright and dark regions. At first, we employ Otsu's method for binarization for gray scale input region. The two largest segments among the bright and the dark regions are selected and the ratio of their sizes is defined as the first measure for color polarity classification. Again, we select the segments that have the smallest standard deviation of the distance from the center among two groups of regions and evaluate the ratio of their standard deviation as the second measure. We use these two ratio features to determine the text color polarity. The proposed method robustly classify color polarity of the text. which has shown by experimental result for the various font and size.

• Science of Emotion and Sensibility
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• v.4 no.2
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• pp.57-62
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• 2001

In the car design process, Interior color scheme is one of the important factors that determined consumer's emotional evaluation with a car styling. The systematic research on the car interior color planning may be not achieved in spite of its importance because it is difficult to simulate color scheme before deciding final prototype. The various alternative of color scheme can be simulated and evaluated in early stage of car design process based on upgraded performance of computer hardware and advance41 co-work system. This study proposed a color coordinate support system for car interior color design to support designer based on emotional scale of color image. Color coordinate support system have four kinds of module, that is, the information acquisition module for gathering user's emotional data, the evaluation module for analyzing relation of color impressions and color attributes, the simulation module for supporting color coordinate design, and the evaluation support module for testing final color alternatives.

• Transactions on Electrical and Electronic Materials
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• v.18 no.6
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• pp.341-344
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• 2017

Infrared (IR) filters were developed to implement integrated three-dimensional (3D) image sensors that are capable of obtaining both color image and depth information at the same time. The combination of light filters applicable to the 3D image sensor is composed of a modified IR cut filter mounted on the objective lens module and on-chip filters such as IR pass filters and color filters. The IR cut filters were fabricated by inorganic $SiO_2/TiO_2$ multilayered thin-film deposition using RF magnetron sputtering. On-chip IR pass filters were synthetized by dissolving various pigments and dyes in organic solvents and by subsequent patterning with photolithography. The fabrication process of the filters is fairly compatible with the complementary metal oxide semiconductor (CMOS) process. Thus, the IR cut filter and IR pass filter combined with conventional color filters are considered successfully applicable to 3D image sensors.

• The KIPS Transactions:PartA
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• v.8A no.3
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• pp.227-234
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• 2001

In this paper we propose a block-type parallel preconditioner for solving large sparse nonsymmetric linear systems, which we expect to be scalable. It is Multi-Color Block SOR preconditioner, combined with direct sparse matrix solver. For the Laplacian matrix the SOR method is known to have a nondeteriorating rate of convergence when used with Multi-Color ordering. Since most of the time is spent on the diagonal inversion, which is done on each processor, we expect it to be a good scalable preconditioner. We compared it with four other preconditioners, which are ILU(0)-wavefront ordering, ILU(0)-Multi-Color ordering, SPAI(SParse Approximate Inverse), and SSOR preconditiner. Experiments were conducted for the Finite Difference discretizations of two problems with various meshsizes varying up to $1025{\times}1024$. CRAY-T3E with 128 nodes was used. MPI library was used for interprocess communications, The results show that Multi-Color Block SOR is scalabl and gives the best performances.

• Journal of the Korea Society of Computer and Information
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• v.16 no.12
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• pp.43-48
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• 2011

Among various methods proposed earlier, fuzzy image filtering is usually one of the favored techniques because it has less blurring effect and the decrease of noise removal rate after filtering. However, fuzzy filtering is ineffective on color images since it is firstly developed with gray scale. Thus, in this paper, we propose a fuzzy filtering algorithm for color images. First, we divide RGB color information from image into three channels of R, G, and B and judge the possibility of each pixel with mask by fuzzy logic independently. The output pixel value might be the average or median according to the degree of noise. Our experiment successfully verifies the effectiveness of new algorithm in color image.

• Fashion & Textile Research Journal
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• v.7 no.5
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• pp.527-534
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• 2005

The purpose of this research is to study face images according to color of make-up was made by computer graphic simulation. The various facial images can be helpful for choosing suitable make-up color planning. In order to find out the differences of face images by make-up color, three different foundations and seven eye-shadows, six lips were applied on the round face model. Make-up Image Scale was used the scale of seven point modified the S-D method. Data were analyzed by Varimax perpendicular rotation method, Duncan's Multiple Range Test, Three-way ANOVA. As the result of make-up image perception analysis, a factor structure was divided into mildness, modernness, elegance, unique. The factor of mildness, modernness, unique affected on the foundation color. Foundation color was found out to be influential variable to distinguish color perception abilities. Also, the foundation, eye-shadow, lip color were influenced interactively on the perception of elegance factor. Pink color was important color, influenced on the mildness factor. Gray and purple color were influenced on the modernness factor. Mildness factor was perceived as the most bright foundation but unique factor was perceived as the most dark foundation. Then, the foundation, eye-shadow, lip color were influenced interactively on the perception of facial images. The results can be effectively applied to today's marketing and color design management which is focused on the product's emotional image in customer's mind.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.972-980
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• 2022

The butterfly species recognition technology based on machine learning using images has the effect of reducing a lot of time and cost of those involved in the related field to understand the diversity, number, and habitat distribution of butterfly species. In order to improve the accuracy and time efficiency of butterfly species classification, various features used as the inputs of machine learning models have been studied. Among them, branch length similarity(BLS) entropy or color intensity entropy methods using the concept of entropy showed higher accuracy and shorter learning time than other features such as Fourier transform or wavelet. This paper proposes a feature extraction algorithm using RGB color intensity entropy for butterfly color images. In addition, we develop butterfly recognition systems that combines the proposed feature extraction method with representative ensemble models and evaluate their performance.