• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5507-5528
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• 2016

In computer vision, salient object is important to extract the useful information of foreground. With active contour analysis acting as the core in this paper, we propose a bottom-up saliency detection algorithm combining with the Bayesian model and the global color distribution. Under the supports of active contour model, a more accurate foreground can be obtained as a foundation for the Bayesian model and the global color distribution. Furthermore, we establish a contour-based selection mechanism to optimize the global-color distribution, which is an effective revising approach for the Bayesian model as well. To obtain an excellent object contour, we firstly intensify the object region in the source gray-scale image by a seed-based method. The final saliency map can be detected after weighting the color distribution to the Bayesian saliency map, after both of the two components are available. The contribution of this paper is that, comparing the Harris-based convex hull algorithm, the active contour can extract a more accurate and non-convex foreground. Moreover, the global color distribution can solve the saliency-scattered drawback of Bayesian model, by the mutual complementation. According to the detected results, the final saliency maps generated with considering the global color distribution and active contour are much-improved.

• The Research Journal of the Costume Culture
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• v.22 no.6
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• pp.997-1010
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• 2014

The purpose of this study is to propose the information and idea that can be applied to color planning by investigating preferred color according to ages, items by distribution of 30s~50s women. As the survey method, questionnaire survey and one-to-one investigation by preparing the color table of basic color and trend color were conducted, and total of 280 questionnaires were used for statistical analysis. The results of this study areas follows. First, in basic color, there were significant differences in preferred hues and tones by age groups. The preferred hues were N, PB in all ages, and the higher the age, the more people preferred vivid tones. Second, in basic color, there was significant difference in preferred hues of jumpers, one-pieces by distribution, and the preferred hue was found to be N in the items except jumpers. There were significant differences in preference tones of all items according to distribution. Third, in trend color, there was no significant difference in preferred hues by age groups. Preferred tones had significant differences by distribution, and the higher the age, the more people preferred vivid tones. Fourth, in trend color, some items had significant differences in preferred hues and tones by distribution. B was preferred in all age groups, and vivid tones were more preferred in wholesaler than the department store. Based on these results, this study proposed 30s~50s women's wear color planning idea that applied consumers' preferred color according to ages and items by distribution.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1677-1683
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• 2006

Color image segmentation is useful for fast retrieval in large image database. For that purpose, new image segmentation technique based on the probability of pixel distribution in the image is proposed. Color image is first divided into R, G, and B channel images. Then, pixel distribution from each of channel image is extracted to select to which it is similar among the well known probabilistic distribution function-Weibull, Exponential, Beta, Gamma, Normal, and Uniform. We use sum of least square error to measure of the quality how well an image is fitted to distribution. That P.d.f has minimum score in relation to sum of square error is chosen. Next, each image is quantized into 4 gray levels by applying thresholds to the c.d.f of the selected distribution of each channel. Finally, three quantized images are combined into one color image to obtain final segmentation result. To show the validity of the proposed method, experiments on some images are performed.

• Publications of The Korean Astronomical Society
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• v.30 no.2
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• pp.261-263
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• 2015

The metallicity distribution of globular clusters (GCs) provides a crucial clue for the star formation history of their host galaxy. With the assumption that GCs are generally old, GC colors have been used as a proxy for GC metallicities. Bimodal color distributions of GCs observed in most large galaxies have, for decades, been interpreted as bimodal metallicity distributions, indicating the presence of two populations within a galaxy. However, the conventional view has been challenged by a new theory that non-linear GC color-metallicity relations can cause a bimodal color distribution even from a single-peaked metallicity distribution. Using photometric and spectroscopic data of NGC 5128 GCs in combination with stellar population simulation models, we examine the effect of non-linearity in GC color-metallicity relations on transformation of the color distributions into the metallicity distributions. Although in some colors offsets are present between observations and models for the color-metallicity relations, their overall shape agrees well for various colors. After the offsets are corrected, the observed spectroscopic metallicity distribution is well reproduced via modeled color-metallicity relations from various color distributions having different morphologies. We discuss the implications of our results.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.49.1-49
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• 2001

The skin color model is a very important concept in face detection, face recognition and face tracking. Usually, this model is obtained by estimating a probability density function of skin color distribution. In many cases, it is assumed that the underlying density function follows a Gaussian distribution. In this paper, a new method for non-parametric estimation of the probability density function, by using feed-forward neural network, is used to estimate the underlying skin color model. By using this method, the resulting skin color model is better than the Gaussian estimation and substantially approaches the real distribution. Applications to face detection and face ...

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2000.06a
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• pp.54-61
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• 2000

A method and results of classification of 4 types metallic wear debris were presented by using their color features. The color image of wear debris was used (or the initial data, and the color properties of the debris were specified by HSI color model. Particle was characterized by a set of statistical features derived from the distribution of HSI color model components. The initial feature set was optimized by a principal component analysis, and multidimensional scaling procedure was used for the definition of classification plane. It was found that five features, which include mean values of H and S, median S, skewness of distribution of S and I, allow to distinguish copper based alloys, red and dark iron oxides and steel particles. In this work, a method of probabilistic decision-making of class label assignment was proposed, which was based on the analysis of debris-coordinates distribution in the classification plane. The obtained results demonstrated a good availability for the automated wear particle analysis.

• KSTLE International Journal
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• v.1 no.1
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• pp.34-42
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• 2000

A method and results of classification of four different metallic wear debris were presented by using their color features. The color image of wear debris was used far the initial data, and the color properties of the debris were specified by HSI color model. Particles were characterized by a set of statistical features derived from the distribution of HSI color model components. The initial feature set was optimized by a principal component analysis, and multidimensional scaling procedure was used fer the definition of a classification plane. It was found that five features, which include mean values of H and S, median S, skewness of distribution of S and I, allow to distinguish copper based alloys, red and dark iron oxides and steel particles. In this work, a method of probabilistic decision-making of class label assignment was proposed, which was based on the analysis of debris-coordinates distribution in the classification plane. The obtained results demonstrated a good availability for the automated wear particle analysis.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.62.1-62.1
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• 2014

Metallicity distribution of globular clusters (GCs) provides an important clue for star formation history of their host galaxy. With an assumption that GCs are generally old, GC colors have been used as a proxy of GC metallicities. Bimodal GC color distributions observed in most large galaxies have, for decades, been interpreted as bimodal metallicity distributions, indicating the presence of two populations within a galaxy. However, the conventional view has been challenged by a new theory that non-linear GC color-metallicity relations (CMRs) can cause a bimodal color distribution even from a single-peaked metallicity distribution. Using the photometric and spectroscopic data of NGC 5128 GCs in combination with stellar population simulation models, we examine the effect of non-linearity in GC CMRs on the transformation of GC color distributions into metallicity distributions. Although, in some colors, offsets are present between observations and models in the CMRs, their overall shape agrees well for various colors. After the offsets are corrected, the observed spectroscopic metallicity distribution is well reproduced via modeled CMRs from various color distributions having different morphologies. On the other hand, the linearly converted metallicity distributions from GC colors show a significant discrepancy with the observed spectroscopic metallicity distribution. We discuss the implications of our results.

• Journal of Distribution Science
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• v.15 no.3
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• pp.39-47
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• 2017

Purpose - The purpose of this study is to measure the effect of brand awareness by color marketing to purchase and revisit intentions in Traditional Markets. Research design, data, and methodology - For this study, 5 point Likert-scale was used based on previous research. Used SPSS ver.22, factor analysis and Cronbach's alpha, regression and correlation were tested. 254 samples were used for the analysis. Results - The three attributes of color marketing(symbolism, identifiability, association) exerted significant effects on brand awareness of traditional marketing explained 38.7% of the variance. Thus, , , and were supported. However, was not supported. Conclusions - Colors play important roles in establishing new images in consumers' minds. The visual sense affects emotions and attitudes and most of the visual sense is affected by colors. Colors that we see move people's heart and induce atmospheres thereby greatly affecting humans' physical and mental activities. To increase traditional market brand awareness, it is necessary for traditional markets to display a level of attractiveness through the use of colors and visuals. So to use color marketing in traditional market is very important for brand awareness which can cause purchase and traditional market revisit intension.

• Journal of the Korean Society of Clothing and Textiles
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• v.22 no.5
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• pp.607-616
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• 1998

The purpose of this study was to develope a practical color range for the fashion industry in Korea. 3623 color sample used by 109 brands from'93 spring/summer to'96 spring/summer were collected. Also, the application frequency and consumer responses of each collected colorwere analyzed in this study. To clarify the characterstics of color range used in the fashion industry, density of color was etermined by color clustering based on A: and consumer responses. The resultsof this study were as follow; 1. The result analyzed based on the Munsell System revealed that the distributions of hue were concentrated in R, YR, Y, and PB. The chroma was concenturated mostly in the low range and the value, on the contrary, showed relatively even distribution. 2. 458 representative colors were selected by color clustering based on the toEless than 1. 0. In addition, the frequency of color applications and consumer responses were also considered when selecting the colors frequently used in the fashion industry and finals, 1163 colors wereobtained. The color range of the representative colors showed similar color characteristics with the 3623 colors supplied by the fashion industry. As a results, this approach was proven to appropriate to develope a color range for a practical use. 3. In conclusion, around 1200 colors were suggested to organize the color range to reflect the characteristics of the fashion industry with an acceptable color differance for color planning and management.