• Title/Summary/Keyword: fuzzy clustering

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Fire Detection Approach using Robust Moving-Region Detection and Effective Texture Features of Fire (강인한 움직임 영역 검출과 화재의 효과적인 텍스처 특징을 이용한 화재 감지 방법)

  • Nguyen, Truc Kim Thi;Kang, Myeongsu;Kim, Cheol-Hong;Kim, Jong-Myon
    • Journal of the Korea Society of Computer and Information
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    • v.18 no.6
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    • pp.21-28
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    • 2013
  • This paper proposes an effective fire detection approach that includes the following multiple heterogeneous algorithms: moving region detection using grey level histograms, color segmentation using fuzzy c-means clustering (FCM), feature extraction using a grey level co-occurrence matrix (GLCM), and fire classification using support vector machine (SVM). The proposed approach determines the optimal threshold values based on grey level histograms in order to detect moving regions, and then performs color segmentation in the CIE LAB color space by applying the FCM. These steps help to specify candidate regions of fire. We then extract features of fire using the GLCM and these features are used as inputs of SVM to classify fire or non-fire. We evaluate the proposed approach by comparing it with two state-of-the-art fire detection algorithms in terms of the fire detection rate (or percentages of true positive, PTP) and the false fire detection rate (or percentages of true negative, PTN). Experimental results indicated that the proposed approach outperformed conventional fire detection algorithms by yielding 97.94% for PTP and 4.63% for PTN, respectively.

Segmentation of Multispectral MRI Using Fuzzy Clustering (퍼지 클러스터링을 이용한 다중 스펙트럼 자기공명영상의 분할)

  • 윤옥경;김현순;곽동민;김범수;김동휘;변우목;박길흠
    • Journal of Biomedical Engineering Research
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    • v.21 no.4
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    • pp.333-338
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    • 2000
  • In this paper, an automated segmentation algorithm is proposed for MR brain images using T1-weighted, T2-weighted, and PD images complementarily. The proposed segmentation algorithm is composed of 3 step. In the first step, cerebrum images are extracted by putting a cerebrum mask upon the three input images. In the second step, outstanding clusters that represent inner tissues of the cerebrum are chosen among 3-dimensional(3D) clusters. 3D clusters are determined by intersecting densely distributed parts of 2D histogram in the 3D space formed with three optimal scale images. Optimal scale image is made up of applying scale space filtering to each 2D histogram and searching graph structure. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram and searching graph structure. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram. In the final step, cerebrum images are segmented using FCM algorithm with its initial centroid value as the outstanding clusters centroid value. The proposed cluster's centroid accurately. And also can get better segmentation results from the proposed segmentation algorithm with multi spectral analysis than the method of single spectral analysis.

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