• Title, Summary, Keyword: Color Clustering

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A Lip Detection Algorithm Using Color Clustering (색상 군집화를 이용한 입술탐지 알고리즘)

  • Jeong, Jongmyeon
    • Journal of the Korea Society of Computer and Information
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    • v.19 no.3
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    • pp.37-43
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    • 2014
  • In this paper, we propose a robust lip detection algorithm using color clustering. At first, we adopt AdaBoost algorithm to extract facial region and convert facial region into Lab color space. Because a and b components in Lab color space are known as that they could well express lip color and its complementary color, we use a and b component as the features for color clustering. The nearest neighbour clustering algorithm is applied to separate the skin region from the facial region and K-Means color clustering is applied to extract lip-candidate region. Then geometric characteristics are used to extract final lip region. The proposed algorithm can detect lip region robustly which has been shown by experimental results.

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.

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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    • 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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A Robust Color Clustering using a Smooth Color Model under Irregular Brightness Variations (Smooth Color Model을 이용한, 불규칙한 조명 변화에 강인한 Color Clustering)

  • Kim, Chi-Ho;You, Bum-Jae;Kim, Hag-Bae;Oh, Sang-Rok
    • Proceedings of the KIEE Conference
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    • pp.2534-2536
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    • 2003
  • Color는 다른 물체로부터 하나의 물체를 특정짓기 위한 효과적이고 강인한 실마리이므로 color clustering이 많은 주목을 받고 있다. 그러나 불규칙한 조명변화에 의한 color 변이 때문에 color segmentation은 매우 어렵다. 이 논문은 B-spline 곡선을 이용한, HSI color space에서의 intensity 정보를 포함한 신뢰할 수 있는 color modeling 방법을 제안한다. 이것은 비록 HS 평균임에도 불구하고 단색 물체의 color 분포가 조명이 변함에따라 변한다는 사실에 기반한다. 이 접근법을 사용하면 피부색을 가진 영역의 color clustering이 불규칙한 조명변화에 적응될 수 있다.

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A Study on Clustering and Color Difference Evaluation of Color Image using HSV Color Space (HSV색공간을 이용한 칼라화상의 클러스터링 및 색차평가에 관한 연구)

  • Kim, Young-Il
    • Journal of the Korean Institute of Telematics and Electronics T
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    • v.35T no.2
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    • pp.20-27
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    • 1998
  • This paper describes color clustering method based on color difference in the uniform Munsell color space obtained from hue, saturation, and value. The proposed method operates in the uniform HSV color space which is approximated using ${L^*}{a^*}{b^*}$ coordinate system based on the RGB inputs. A clustering and color difference evaluation are proposed by thresholding NBS unit which is likely to Balinkin color difference equation. Region segmentation and isolation process are carried out ISO DATA algorithm which is a self iterative clustering technique. Through the clustering of 2 input images according to the threshold value, satisfactory results are obtained. So, in conclusion, it is possible to extract result of better region segmentation using human color perception of the objects.

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Hand Segmentation Using Depth Information and Adaptive Threshold by Histogram Analysis with color Clustering

  • Fayya, Rabia;Rhee, Eun Joo
    • Journal of Korea Multimedia Society
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    • v.17 no.5
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    • pp.547-555
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    • 2014
  • This paper presents a method for hand segmentation using depth information, and adaptive threshold by means of histogram analysis and color clustering in HSV color model. We consider hand area as a nearer object to the camera than background on depth information. And the threshold of hand color is adaptively determined by clustering using the matching of color values on the input image with one of the regions of hue histogram. Experimental results demonstrate 95% accuracy rate. Thus, we confirmed that the proposed method is effective for hand segmentation in variations of hand color, scale, rotation, pose, different lightning conditions and any colored background.

VS-FCM: Validity-guided Spatial Fuzzy c-Means Clustering for Image Segmentation

  • Kang, Bo-Yeong;Kim, Dae-Won
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.10 no.1
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    • pp.89-93
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    • 2010
  • In this paper a new fuzzy clustering approach to the color clustering problem has been proposed. To deal with the limitations of the traditional FCM algorithm, we propose a spatial homogeneity-based FCM algorithm. Moreover, the cluster validity index is employed to automatically determine the number of clusters for a given image. We refer to this method as VS-FCM algorithm. The effectiveness of the proposed method is demonstrated through various clustering examples.

Fish Injured Rate Measurement Using Color Image Segmentation Method Based on K-Means Clustering Algorithm and Otsu's Threshold Algorithm

  • Sheng, Dong-Bo;Kim, Sang-Bong;Nguyen, Trong-Hai;Kim, Dae-Hwan;Gao, Tian-Shui;Kim, Hak-Kyeong
    • Journal of Power System Engineering
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    • v.20 no.4
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    • pp.32-37
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    • 2016
  • This paper proposes two measurement methods for injured rate of fish surface using color image segmentation method based on K-means clustering algorithm and Otsu's threshold algorithm. To do this task, the following steps are done. Firstly, an RGB color image of the fish is obtained by the CCD color camera and then converted from RGB to HSI. Secondly, the S channel is extracted from HSI color space. Thirdly, by applying the K-means clustering algorithm to the HSI color space and applying the Otsu's threshold algorithm to the S channel of HSI color space, the binary images are obtained. Fourthly, morphological processes such as dilation and erosion, etc. are applied to the binary image. Fifthly, to count the number of pixels, the connected-component labeling is adopted and the defined injured rate is gotten by calculating the pixels on the labeled images. Finally, to compare the performances of the proposed two measurement methods based on the K-means clustering algorithm and the Otsu's threshold algorithm, the edge detection of the final binary image after morphological processing is done and matched with the gray image of the original RGB image obtained by CCD camera. The results show that the detected edge of injured part by the K-means clustering algorithm is more close to real injured edge than that by the Otsu' threshold algorithm.

A study on the color image segmentation using the fuzzy Clustering (퍼지 클러스터링을 이용한 칼라 영상 분할)

  • 이재덕;엄경배
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.109-112
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    • 1999
  • Image segmentation is the critical first step in image information extraction for computer vision systems. Clustering methods have been used extensively in color image segmentation. Most analytic fuzzy clustering approaches are divided from the fuzzy c-means(FCM) algorithm. The FCM algorithm uses fie probabilistic constraint that the memberships of a data point across classes sum to 1. However, the memberships resulting from the FCM do not always correspond to the intuitive concept of degree of belonging or compatibility. Moreover, the FCM algorithm has considerable trouble under noisy environments in the feature space. Recently, a possibilistic approach to clustering(PCM) for solving above problems was proposed. In this paper, we used the PCM for color image segmentation. This approach differs from existing fuzzy clustering methods for color image segmentation in that the resulting partition of the data can be interpreted as a possibilistic partition. So, the problems in the FCM can be solved by the PCM. But, the clustering results by the PCM are not smoothly bounded, and they often have holes. The region growing was used as a postprocessing after smoothing the noise points in the pixel seeds. In our experiments, we illustrate that the PCM us reasonable than the FCM in noisy environments.

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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
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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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