• Title/Summary/Keyword: Fuzzy color

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Color Data Clustering Algorithm using Fuzzy Color Model (퍼지컬러 모델을 이용한 컬러 데이터 클러스터링 알고리즘1)

  • Kim, Dae-Won;Lee, Kwang H.
    • 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.

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On Color Cluster Analysis with Three-dimensional Fuzzy Color Ball

  • Kim, Dae-Won
    • 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.

A Note on the Defuzzification Method and Distance Metric of Fuzzy Color Model (퍼지 컬러 모델의 비퍼지화 방법과 거리 척도의 제안)

  • Kim, Dae-Won;Lee, Kwang H.
    • Proceedings of the Korean Information Science Society Conference
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    • 2001.10b
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    • pp.40-42
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    • 2001
  • Most people have to deal with color and color problems occasionally. There are many strange things about color and color vision that most people do not notice. Even though color seems intuitive and simple it is not. In this paper, we modeled the color using fuzzy set theory. The proposed fuzzy color model is based on the Munsell color space. We defined several fuzzy color terminologies, and proposed a extended center of gravity defuzzification mthod for fuzzy color set. Finally, three distance measures between fuzzy colors were also formulated.

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Fuzzy Control of Anti -Sway Motion for a Remote Crane Operation

  • Park, Sun-Won;Kang, E-Sok
    • 제어로봇시스템학회:학술대회논문집
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    • 2001.10a
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    • pp.42.1-42
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    • 2001
  • This paper presents a fuzzy-based method for classification skin color object in a complex background under varying illumination. Parameters of fuzzy rule base are generated using a genetic algorithm(GA). The color model is used in the YCbCr color space. We propose a unique fuzzy system in order to accommodate varying background color and illumination condition. This fuzzy system approach to skin color classification is discussed along with an overview of YCbCr color space.

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Intelligent and Robust Face Detection

  • Park, Min-sick;Park, Chang-woo;Kim, Won-ha;Park, Mignon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.11 no.7
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    • pp.641-648
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    • 2001
  • A face detection in color images is important for many multimedia applications. It is first step for face recognition and can be used for classifying specific shorts. This paper describes a new method to detect faces in color images based on the skin color and hair color. This paper presents a fuzzy-based method for classifying skin color region in a complex background under varying illumination. The Fuzzy rule bases of the fuzzy system are generated using training method like a genetic algorithm(GA). We find the skin color region and hair color region using the fuzzy system and apply the convex-hull to each region and find the face from their intersection relationship. To validity the effectiveness of the proposed method, we make experiment with various cases.

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Skin Color Extraction in Varying Backgrounds and illumination Conditions

  • Park, Minsick;Park, Chang-Woo;Kim, Won-ha;Park, Mignon
    • 제어로봇시스템학회:학술대회논문집
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    • 2001.10a
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    • pp.162.4-162
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    • 2001
  • This paper presents a fuzzy-based method for classification skin color object in a complex background under varying illumination Parameters of fuzzy rule base are generated using a genetic algorithm(GA). The color model is used in the YCbCr color space. We propose a unique fuzzy system in order to accommodate varying background color and illumination condition This fuzzy system approach to skin color classification is discussed along with an overview of YCbCr color space.

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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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Extraction of Facial Region Using Fuzzy Color Filter (퍼지 색상 필터를 이용한 얼굴 영역 추출)

  • Kim, M.H.;Park, J.B.;Jung, K.H.;Joo, Y.H.;Lee, J.;Cho, Y.J.
    • Proceedings of the KIEE Conference
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    • 2004.11c
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    • pp.147-149
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    • 2004
  • There are no authentic solutions in a face region extraction problem though it is an important part of pattern recognition and has diverse application fields. It is not easy to develop the facial region extraction algorithm because the facial image is very sensitive according to age, sex, and illumination. In this paper, to solve these difficulties, a fuzzy color filer based on the facial region extraction algorithm is proposed. The fuzzy color filter makes the robust facial region extraction enable by modeling the skin color. Especially, it is robust in facial region extraction with various illuminations. In addition, to identify the fuzzy color filter, a linear matrix inequality(LMI) optimization method is used. Finally, the simulation result is given to confirm the superiority of the proposed algorithm.

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Human Sensory of Perception Oriented Color Fuzzy Image Processing (인간의 감성을 고려한 칼라 Fuzzy 영상처리)

  • 박영현;김승우;우동민;박민용
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.29B no.3
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    • pp.47-54
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    • 1992
  • This paper presents an image processing method which emphasizes a certain perception feeling on a color image. A series of fuzzy image processing schemes are proposed, which transform an color image represented in the LCH coordinate system to the image with a certain perception feeling based on the rules and fuzzy inference. To obtain the rules, results of the sensory perception tests are applied to the quantification theory. The proposed schemes can be effectively utilized in the color copy system, since printed outputs possibly look better than originals.

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Segmentation of Color Image by Subtractive and Gravity Fuzzy C-means Clustering (차감 및 중력 fuzzy C-means 클러스터링을 이용한 칼라 영상 분할에 관한 연구)

  • Jin, Young-Goun;Kim, Tae-Gyun
    • Journal of IKEEE
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    • v.1 no.1 s.1
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    • pp.93-100
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    • 1997
  • In general, fuzzy C-means clustering method was used on the segmentation of true color image. However, this method requires number of clusters as an input. In this study, we suggest new method that uses subtractive and gravity fuzzy C-means clustering. We get number of clusters and initial cluster centers by applying subtractive clustering on color image. After coarse segmentation of the image, we apply gravity fuzzy C-means for optimizing segmentation of the image. We show efficiency of the proposed algorithm by qualitative evaluation.

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