• Journal of The Korean Astronomical Society
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• v.10 no.1
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• pp.31-38
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• 1977

To reduce the instrumental and calibration errors in the photoelectric photometry as much as possible it is necessary to select the optimum photocell voltage and energy attenaution and to observe as many standard stars as possible over the wide range of color, spectral type and air mass.

• The Journal of Advanced Prosthodontics
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• v.5 no.3
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• pp.296-304
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• 2013

PURPOSE. The aim of this study was to evaluate the effect of polishing and glazing on the color and spectral distribution of monolithic zirconia. MATERIALS AND METHODS. Forty-five monolithic zirconia specimens ($16.3mm{\times}16.4mm{\times}2.0mm$) were fabricated and divided into 5 groups according to the number of A2-coloring liquid applications (Group I to V). Each group was divided into 3 subgroups according to the method of surface treatments (n=3): N: no treatment; P: polishing; G: glazing. Color and spectral distribution of five different areas of each specimen were measured according to CIELAB color space in the reflectance mode relative to the standard illuminant D65 on a reflection spectrophotometer. Data were analyzed using one-way ANOVA followed by Tukey's HSD test, Pearson correlation and regression analysis (${\alpha}$=.05). RESULTS. There was a significant difference in CIE $L^*$ between Subgroup N and P, and in CIE $b^*$ between Subgroup P and G in each group. Spectral reflectance generally decreased in Subgroup P and G in comparison with Subgroup N. Color differences between Subgroup P and G were within the perceptibility threshold (${\Delta}E{^*}_{ab}$ < 3.7) in most groups. Highly significant correlation was found between CIE $b^*$ and each subgroups as the number of coloring liquid applications increased ($R^2$ >0.88, P<.001). CONCLUSION. A perceptible color difference can be detected after polishing of monolithic zirconia. Polishing decreases the lightness, and glazing also decreases the lightness, but increases the yellowness of monolithic zirconia.

• Korean Journal of Optics and Photonics
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• v.20 no.2
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• pp.110-117
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• 2009

This work introduces the uncertainty evaluation formulation on color measurement of light sources and display devices, such as CIE 1931 (x, y) chromaticity, CIE 1960 (u, v) chromaticity, correlated color temperature, and distribution temperature. All the mentioned quantities are reduced from spectral data in the visible range, for which uncertainties are strongly correlated between different wavelengths. Using matrix algebra we have formulated the uncertainty propagation from the SI- traceable spectral irradiance standard to the individual color related measurement quantities taking the correlation between wavelengths into account. As a result, we have demonstrated uncertainty evaluation examples of 3 types of light sources: CIE illuminant A, LED white light, and LCD white light. This method can be applied to any other quantities based on spectral measurement such as solar irradiance, material color measurement, etc.

• Journal of the Korea Society of Computer and Information
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• v.19 no.2
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• pp.49-56
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• 2014

This thesis proposes CBIRS/TB method that uses a tablet's color distribution information and form distinctive in content-based search. CBIRS/TB can avoid misuses and improper tablet uses by conducting content-based search in commonly prescribed tablets. The existing FE-CBIRS system is limited to recognizing only the image of color and shape of the tablet, that leads to applying insufficient form-specific information. While CBIRS/TB utilizes average, standard deviation, hue and saturation of each tablets in color, brightness, and contrast, FE-CBIRS has partial-sphere application problem; only applying the typical color of the tablet. Also, in case of the shape-specific-information, Invariant Moment is mainly used for the extracted partial-spheres. This causes delayed processing time and accuracy problems. Therefore, to improve this setback, this thesis indexed color-specific-information of the extracted images into categorized classification for improved search speed and accuracy.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.4
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• pp.161-168
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• 2006

In this paper, we propose a method for the pattern inspection of LCD module using the color compensation and pattern matching. The pattern matching is generally used for the inspection method of LCD module at the industry. LCD module has many defections such as the brightness difference of the back light, the optic feature of liquid crystal, the difference of the light penetrated by driving LCD and the color difference by the lighting. The conventional method without the color compensation can not solve these defections and decreases the efficiency of inspecting LCD module. The method proposed to inspect defective badness through the pattern matching after it compensated color difference of the LCD occurred by the various causes. At first, it revises with setting by standard tone of color with the LCD pattern of the reference image. And It perform the preprocessing and pattern matching algorithm on the compensated image. In experiment, we confirmed that this algorithm is useful to detect some defections of LCD module. The proposed methods was easy to detect the faulty product.

• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.91-97
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• 2009

This paper proposes novel FE-CBIRS that finds best position of a cut to be retrieved based on color feature distribution in digital contents of IPTV. Conventional CBIRS have used a method that utilizes both color and shape information together to classify images, as well as a method that utilizes both feature information of the entire region and feature information of a partial region that is extracted by segmentation for searching. Also, in the algorithm, average, standard deviation and skewness values are used in case of color features for each hue, saturation and intensity values respectively. Furthermore, in case of using partial regions, only a few major colors are used and in case of shape features, the invariant moment is mainly used on the extracted partial regions. Due to these reasons, some problems have been issued in CBIRS in processing time and accuracy so far. Therefore, in order to tackle these problems, this paper proposes the FE-CBIRS that makes searching speed faster by classifying and indexing the extracted color information by each class and by using several cuts that are restricted in range as comparative images.

• The KIPS Transactions:PartB
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• v.15B no.5
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• pp.407-412
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• 2008

Color segmentation is one of the most challenging problems in image processing especially in case of handling the images with cluttered background. Great amount of color segmentation methods have been developed and applied to real problems. In this paper, we suggest a new methodology. Our approach is focused on background extraction, as a complimentary operation to standard foreground object segmentation, using self-organizing feature selective property of unsupervised self-learning paradigm based on the competitive algorithm. The results of our studies show that background segmentation can be achievable in efficient manner.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.2
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• pp.65-70
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• 2013

In this paper, we proposed color image watermarking using human visual system. A watermark is embedded by transforming a color image of RGB coordinate into a color image of HSI coordinate with considering that chromatic components are less sensitive than achromatic components. Watermark is embedded in the frequency domain of the chromatic channels by using discrete cosine transform. Watermark is extracted from watermarked image by using inverse discrete cosine transform. To verify the proposed method, a standard image and a fingerprint image are used for the original image and the watermark image, respectively. Simulation results are satisfied with invisibility and robustness from attacks as image compression.

• Asian-Australasian Journal of Animal Sciences
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• v.14 no.6
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• pp.858-861
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• 2001

Near Infrared spectroscopy was applied to the samples of processed pork to see the effect of grinding on chemical components analyses. Data from conventional chemical analyses of moisture, fat, protein, NaCl were put into calibration model by NIR of reflectance mode. The other properties observed were pH and color parameters ($L^*,\;a^*,\;b^*$). Spectral ranges of 400~2500 nm and 400~1100 nm were compared for color parameters. Spectral ranges of 400~2500 nm and 1100~2500 nm were compared for chemical components and pH. Different spectral ranges caused little changes in the coefficients of determination or standard errors. $R^{2,}s$ of calibration models for color parameters were in the range of 0.97 to 1.00. $R^{2,}s$ of calibration models of intact sausages for moisture, protein, fat, NaCl and pH were 0.98, 0.89, 0.95, 0.73 and 0.77, respectively using spectra at 1100~2500 nm. $R^{2,}s$ of calibration models of ground sausages for moisture, protein, fat, NaCl and pH were 0.97, 0.91, 0.97, 0.42 and 0.56, respectively using spectra at 1100~2500 nm.

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.2
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• pp.354-368
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• 2020

Accurate and versatile image data-sets are essential for fashion AI research and AI-based fashion businesses based on a systematic attribute classification system. This study constructs a color and texture attribute hierarchical classification system by collecting fashion item images and analyzing the metadata of fashion items described by consumers. Essential dimensions to explain color and texture attributes were extracted; in addition, attribute values for each dimension were constructed based on metadata and previous studies. This hierarchical classification system satisfies consistency, exclusiveness, inclusiveness, and flexibility. The image tagging to confirm the usefulness of the proposed classification system indicated that the contents of attributes of the same image differ depending on the annotator that require a clear standard for distinguishing differences between the properties. This classification system will improve the reliability of the training data for machine learning, by providing standardized criteria for tasks such as tagging and annotating of fashion items.