• Title/Summary/Keyword: Color model

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A Study on the Color Membership Computation Method using Fuzzy Color Model (패지 컬러 모델을 이용한 컬러의 소속 정도를 결정하는 방법에 관한 연구)

  • Kim, Dae-Won;Lee, Kwang. H.
    • Proceedings of the Korean Information Science Society Conference
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    • 2002.04b
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    • pp.262-264
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    • 2002
  • In this paper we focused on the color representation prob1em based on fuzzy set theory. The main factor is the determination or computation of color membership function and color difference formula. The mathematical formula to calculate the color difference should generate a uniform color scaling, and due to this reason we adopted a CIELAB color- space as a fundamental feature space. With the help of the CIELAB color space we created a new color model, referred to fuzzy color model, which can represent the ambiguous characteristics underlying colors. Based on the proposed color difference formula between fuzzy colors, we could obtain the membership computation method of an arbitrary color for a given color family.

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Recognition of Resistor Color Band Using a Color Segmentation in a HSI Color Model (HSI 색상 모델에서 색상 분할을 이용한 저항 색상 밴드 인식)

  • Jung, Min Chul
    • Journal of the Semiconductor & Display Technology
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    • v.18 no.2
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    • pp.67-72
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    • 2019
  • This paper proposes a new method for the recognition of resistor color band using a color segmentation in a HSI color model. The proposed method firstly segments a resistor in a chromatic color as a ROI from a background. Secondly, the color bands of the resistor are segmented by vertical projection profile using both the intensity and the saturation differentiation and finally, it recognizes the colors of the segmented color bands using hue, saturation and intensity values. The final results are the value of the resistor and the names of the recognized color. The proposed method is implemented using C language in Raspberry Pi system with a camera module for a real-time image processing. Experiments were conducted by using various resistor images. The results show that the proposed method is successful for the recognition of resistor color band.

A Basic Study on CMYK Color Model for Interior Design (실내디자인을 위한 CMYK 모델 색채 팔레트 제안 가능성을 위한 기초 연구 - 1995년 건축상 수상자 사진들을 분석대상으로 C, M, Y의 상관관계 추출)

  • 이현수;김은정;김현경;이승희;조명은
    • Korean Institute of Interior Design Journal
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    • no.27
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    • pp.3-11
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    • 2001
  • The CMYK is the mathematical color model and the Musell system is an intuitive color model. CMYK color model needs to be proposed in the information age. These two models need to be integrated for convenience of color design. This paper deals with CMRK values, which have appeared in interior design, in association with the Munsell code. Firstly, color samples have been extracted from the cases of interior design. Secondly, the CMRK values of the color sample and the Munsell code have been found. Thirdly, relationship between the CMRK values has been formularized in comparison with the Whelan's color pallette by using the regression method. This paper concludes by proposing the formular which can be used to develope color palette in further research.

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Saliency Detection based on Global Color Distribution and Active Contour Analysis

  • Hu, Zhengping;Zhang, Zhenbin;Sun, Zhe;Zhao, Shuhuan
    • 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.

2-Stage Adaptive Skin Color Model for Effective Skin Color Segmentation in a Single Image (단일 영상에서 효과적인 피부색 검출을 위한 2단계 적응적 피부색 모델)

  • Do, Jun-Hyeong;Kim, Keun-Ho;Kim, Jong-Yeol
    • 한국HCI학회:학술대회논문집
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    • 2009.02a
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    • pp.193-196
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    • 2009
  • Most of studies adopt a fixed skin color model to segment skin color region in a single image. The methods, however, result in low detection rates or high false positive error rates since the distribution of skin color is varies depending on the characteristics of input image. For the effective skin color segmentation, therefore, we need a adaptive skin color model which changes the model depending on the color distribution of input image. In this paper, we propose a novel adaptive skin color segmentation algorithm consisting of 2 stages which results in both high detection rate and low false positive error rate.

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Evolutionary Generation Based Color Detection Technique for Object Identification in Degraded Robot Vision (저하된 로봇 비전에서의 물체 인식을 위한 진화적 생성 기반의 컬러 검출 기법)

  • Kim, Kyoungtae;Seo, Kisung
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.64 no.7
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    • pp.1040-1046
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    • 2015
  • This paper introduces GP(Genetic Programming) based color detection model for an object detection of humanoid robot vision. Existing color detection methods have used linear/nonlinear transformation of RGB color-model. However, most of cases have difficulties to classify colors satisfactory because of interference of among color channels and susceptibility for illumination variation. Especially, they are outstanding in degraded images from robot vision. To solve these problems, we propose illumination robust and non-parametric multi-colors detection model using evolution of GP. The proposed method is compared to the existing color-models for various environments in robot vision for real humanoid Nao.

A Perceptually-Adaptive High-Capacity Color Image Watermarking System

  • Ghouti, Lahouari
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.11 no.1
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    • pp.570-595
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    • 2017
  • Robust and perceptually-adaptive image watermarking algorithms have mainly targeted gray-scale images either at the modeling or embedding levels despite the widespread availability of color images. Only few of the existing algorithms are specifically designed for color images where color correlation and perception are constructively exploited. In this paper, a new perceptual and high-capacity color image watermarking solution is proposed based on the extension of Tsui et al. algorithm. The $CIEL^*a^*b^*$ space and the spatio-chromatic Fourier transform (SCFT) are combined along with a perceptual model to hide watermarks in color images where the embedding process reconciles between the conflicting requirements of digital watermarking. The perceptual model, based on an emerging color image model, exploits the non-uniform just-noticeable color difference (NUJNCD) thresholds of the $CIEL^*a^*b^*$ space. Also, spread-spectrum techniques and semi-random low-density parity check codes (SR-LDPC) are used to boost the watermark robustness and capacity. Unlike, existing color-based models, the data hiding capacity of our scheme relies on a game-theoretic model where upper bounds for watermark embedding are derived. Finally, the proposed watermarking solution outperforms existing color-based watermarking schemes in terms of robustness to standard image/color attacks, hiding capacity and imperceptibility.

Smoke color analysis of the standard color models for fire video surveillance (화재 영상감시를 위한 표준 색상모델의 연기색상 분석)

  • Lee, Yong-Hun;Kim, Won-Ho
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.14 no.9
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    • pp.4472-4477
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    • 2013
  • This paper describes the color features of smoke in each standard color model in order to present the most suitable color model for somke detection in video surveillance system. Histogram intersection technique is used to analyze the difference characteristics between color of smoke and color of non smoke. The considered standard color models are RGB, YCbCr, CIE-Lab, HSV, and if the calculated histogram intersection value is large for the considered color model, then the smoke spilt characteristics are not good in that color model. If the calculated histogram intersection value is small, then the smoke spilt characteristics are good in that color model. The analyzed result shows that the RGB and HSV color models are the most suitable for color model based smoke detection by performing respectively 0.14 and 0.156 for histogram intersection value.

Genetic Programming based Illumination Robust and Non-parametric Multi-colors Detection Model (밝기변화에 강인한 Genetic Programming 기반의 비파라미터 다중 컬러 검출 모델)

  • Kim, Young-Kyun;Kwon, Oh-Sung;Cho, Young-Wan;Seo, Ki-Sung
    • Journal of the Korean Institute of Intelligent Systems
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    • v.20 no.6
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    • pp.780-785
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    • 2010
  • This paper introduces GP(Genetic Programming) based color detection model for an object detection and tracking. Existing color detection methods have used linear/nonlinear transformatin of RGB color-model and improved color model for illumination variation by optimization or learning techniques. However, most of cases have difficulties to classify various of colors because of interference of among color channels and are not robust for illumination variation. To solve these problems, we propose illumination robust and non-parametric multi-colors detection model using evolution of GP. The proposed method is compared to the existing color-models for various colors and images with different lighting conditions.

Human Tracking using Multiple-Camera-Based Global Color Model in Intelligent Space

  • Jin Tae-Seok;Hashimoto Hideki
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.6 no.1
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    • pp.39-46
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    • 2006
  • We propose an global color model based method for tracking motions of multiple human using a networked multiple-camera system in intelligent space as a human-robot coexistent system. An intelligent space is a space where many intelligent devices, such as computers and sensors(color CCD cameras for example), are distributed. Human beings can be a part of intelligent space as well. One of the main goals of intelligent space is to assist humans and to do different services for them. In order to be capable of doing that, intelligent space must be able to do different human related tasks. One of them is to identify and track multiple objects seamlessly. In the environment where many camera modules are distributed on network, it is important to identify object in order to track it, because different cameras may be needed as object moves throughout the space and intelligent space should determine the appropriate one. This paper describes appearance based unknown object tracking with the distributed vision system in intelligent space. First, we discuss how object color information is obtained and how the color appearance based model is constructed from this data. Then, we discuss the global color model based on the local color information. The process of learning within global model and the experimental results are also presented.