• Journal of the Korean Society of Clothing and Textiles
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• v.38 no.3
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• pp.347-354
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• 2014

The color appearance of a yarn-dyed woven fabric depends on the color of the yarn as well as on the weave structure. Predicting the final color appearance or formulating the recipe is a difficult task, considering the interference of colored yarns and structure variations. In a modern fabric design process, the intended color appearance is attained through a digital color methodology based on numerous color data and color mixing recipes (i.e., color prediction models, accumulated in CAD systems). For successful color reproduction, accurate color prediction models should be devised and equipped for the systems. In this study, the final colors of yarn-dyed woven fabrics were predicted using six geometric-color mixing models (i.e., simple K/S model, log K/S model, D-G model, S-N model, modified S-N model, and W-O model). The color differences between the measured and the predicted colors were calculated to evaluate the accuracy of various color models used for different weave structures. The log K/S model, D-G model, and W-O model were found to be more accurate in color prediction of the woven fabrics used. Among these three models, the W-O model was found to be the best one as it gave the least color difference between the measured and the predicted colors.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.119-122
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• 2002

The research Interest of this paper is focused on the efficient clustering task for an arbitrary color data. In order to tackle this problem, we have tiled to model the inherent uncertainty and vagueness of color data using fuzzy color model. By laking a fuzzy approach to color modeling, we could make a soft decision for the vague regions between neighboring colors. The proposed fuzzy color model defined a three dimensional fuzzy color ball and color membership computation method with the two inter-color distance measures. With the fuzzy color model, we developed a new fuzzy clustering algorithm for an efficient partition of color data. Each fuzzy cluster set has a cluster prototype which is represented by fuzzy color centroid.

• Journal of the Korean Graphic Arts Communication Society
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• v.18 no.2
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• pp.29-40
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• 2000

Recently. color proofing in printing industry grow rapidly. If an order decide color from known color information in the case of color reproduction, we can reduce expenses and time. In color proofing the best important point must be closed proofed color to primary color and secondary color. Model-based approaches have the advantages of faster recharacterization and the opportunity of simulating product enhancements such as changes in ink properties and halftoning. In this paper, we transformed the dot area of CMYK to CIELAB color space using color reproduction models. Firstly, we measured spectral reflectance of primary color printed by Matchprint II and the data was used to find tone reproduction curve using regression equation, and than we applied at primary color model, such as Murray-Davies, Yule-Nilsen, and mixed color model, such as Kubelka-Munk, relaxed version of spectral Neugebauer. In such results, the Kubelka-Munk model resulted in the best spectral reconstruction accuracy followed by relaxed version of spectral Neugebauer model, color difference is 2.8401.

• Proceedings of the Korean Printing Society Conference
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• 2000.04a
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• pp.25-34
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• 2000

Recently, color proofing in printing industry grow rapidly. If an order decide color from known color information in the case of color reproduction, we can reduce expenses and time. In color proofing the best important point must be closed proofed color to primary color and secondary color. Model-based approaches have the advantages of faster recharacterization and the opportunity of simulating product enhancements such as changes in ink properties and halftoning. In this paper, we transformed the dot area of CMYK to CIELAB color space using color reprodution models. Firstly, we measured spectral reflectance of primary color printed by Matchprint II and the data was used to find tone reproduction curve using regression equation, and than we applied at primary color model, such as Murray-Davies, Yule-Nilsen, and mixed color model, such as Kubelka--Munk, relaxed version of spectral Neugebauser. In such results, the Kubleka-Munk model resulted in the best spectral reconstruction accuracy followed by relaxed version of spectral Neugebauer model, color difference is 2.8401.

• 한국정보디스플레이학회:학술대회논문집
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• 2009.10a
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• pp.1154-1157
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• 2009

This template provides you with an example of the The CIE Color Appearance Model (CIECAM02) is now widely used for various digital imaging systems including digital displays. The CIECAM02 were intended to be an empirical model, however, some aspects of the model are closely related to the human color vision mechanism. This paper will discuss the relationship between human color vision and color imaging.

• Korean Institute of Interior Design Journal
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• no.31
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• pp.64-70
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• 2002

One of the main motivations of this research process is to develop an adaptable VR model whose color can be changed according to the emotional information of user. This paper addresses how to define color scheme and combine colors with harmony. The adaptable color of the VR model consists of three processes: emotional keyword identification, the color combination and the VR model adaptation processes. We have used the biorhythm to derive the emotional keyword which is used to find the color harmony scheme. The color harmony scheme provides information for the color combination of the VR model. Finally, we have obtained the VR model which color has been changed using the identified color schema.

• The Journal of the Korea Contents Association
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• v.9 no.5
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• pp.1-12
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• 2009

The correct detection of skin color is an important preliminary process in fields of face detection and human motion analysis. It is generally performed by three steps: transforming the pixel color to a non-RGB color space, dropping the illuminance component of skin color, and classifying the pixels by the skin color distribution model. Skin detection depends on by various factors such as color space, presence of the illumination, skin modeling method. In this paper we propose a 3d skin color model that can segment pixels with several ethnic skin color from images with various illumination condition and complicated backgrounds. This proposed skin color model are formed with each components(Y, Cb, Cr) which transform pixel color to YCbCr color space. In order to segment the skin color of several ethnic groups together, we first create the skin color model of each ethnic group, and then merge the skin color model using its skin color probability. Further, proposed model makes several steps of skin color areas that can help to classify proper skin color areas using small training data.

• Journal of Digital Contents Society
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• v.4 no.1
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• pp.11-21
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• 2003

In computer vision, the goal of stereopsis is to determine the surface structure of real world form two or more perspective views of scene. It is similar to human visual system. We can avoid obstacles, recognize objects, and manipulate machine using three-dimensional information. Until recently, only gray-level images have been used as input to computation for depth determination, but the availability of color can further enhance the performance of computational stereopsis. There are many models to provide efficient color system. The simplest model, RGB model treats color as if it were composed of separate entities. Each color channel is processed individually by the same stereopsis module as used in the gray-level model. His Model decouples intensity component from color information. So it can deal with color properties without defect intensity information. Opponent color model is based on human visual system. In this model, the red-green-blue colors are combined into three opponent channels before further processing.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.262-267
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• 2008

The focus of this paper is on devising an efficient clustering task for arbitrary color data. In order to tackle this problem, the inherent uncertainty and vagueness of color are represented by a fuzzy color model. By taking a fuzzy approach to color representation, the proposed model makes a soft decision for the vague regions between neighboring colors. A definition on a three-dimensional fuzzy color ball is introduced, and the degree of membership of color is computed by employing a distance measure between a fuzzy color and color data. With the fuzzy color model, a novel fuzzy clustering algorithm for efficient partition of color data is developed.

• Journal of the Korean Society of Clothing and Textiles
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• v.42 no.1
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• pp.121-132
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• 2018

Recent beauty trends focus on suitability to individual features. A personal color system is a recent aesthetic concept that influences color make up and coordination. However, a personal color concept has several weaknesses. For example, type classification is qualitative and not quantitative because its measuring system is a sensory test with no industry standard of personal color system. A quantitative personal color type classification model is the purpose of this study, which can be a solution to above problems. This model is a kind of mapping system in a 3D Cartesian coordinate system which has own axes, Value, Saturation, and Yellowness. The cheek color of the individual sample is also independent variable and personal color type is a dependent variable. In order to construct the model, this study conducted a colorimetric survey on a 993 sampling frequency of Korean women in their 20s and 30s. The significance of this study is as follows. First, through this study, personal color system is established on quantitative color space; in addition, the model has flexibility and scalability because it consisted of independent axis that allows for the inclusion of any other critical variable in the form of variable axis.