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    Cyclone identification using Fuzzy C Mean clustering
    (2013-12-31)
    Warunsin, Kulwarun
    ;
    Chitsobhuk, Orachat
    In this paper, the performance of the cyclone identification system using histogram of wind speed and wind direction from the QuikSCAT satellite is demonstrated. The detections based on support vector machines (SVM) classification and Fuzzy C-Means (FCM) clustering are evaluated. SVM technique makes use of a kernel function for classification, which performs well with datasets having nonlinear boundaries. However, it is difficult to determine the suitable kernel function for each dataset and it is needed to be examined. On the other hand, FCM technique is soft unsupervised clustering, which allows each data element to be in more than one cluster with different membership value. This makes it robust to ambiguity datasets. A database of 90 events; 45 cyclone events and 45 non-cyclone events; from the QuikSCAT satellite data is used for the performance evaluation. The performance of the proposed cyclone identification system is then compared to that of [7]. The experimental results show that cyclone identification using Fuzzy C-Mean clustering outperforms that using SVM technique since the SVM is sensitive to the outliers or noises in the dataset thus leads to a reduction in identification performance. © 2013 IEEE.
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    Medical Image Compression Using Tree-Structured Vector Quantization and Fuzzy C-Means
    (2002-01-01)
    Supot, Sookpotharom
    ;
    Yuttana, Kitjaidure
    ;
    Manas, Sangworasil
    Compression of magnetic resonance images (MRI) has proved to be more difficult than other medical imaging modalities. In an average sized hospital, many tera bytes of digital imaging data (MRI) are generated every year, almost all of which has to be kept. Compression of medical images is currently being performed by using different algorithms. In this paper, Fuzzy Clustering Method is used for the image Tree Structure Vector Quantization (TSVQ). First, MR image is used for the feature vector. Then use this feature vector to design a classification tree by Fuzzy C-Means (FCM) algorithm to split two clusters. At every nonterminal, the centroid of the feature vectors clustered in each child node is computed to be the testing vector. At every leaf, the centroid of the training image blocks corresponding to their feature vectors falling on the same terminal node is calculated to be the codevector. All codevectors in the leaves are composed of a codebook. By doing so, the algorithm can preserve the edge of image, make good image quality, and reduce the processing time while constructing Tree Structured Codebook.
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    Codebook design algorithm for classified vector quantization based on fuzzy clustering
    (2002-01-01)
    Supot, Sookpotharom
    ;
    Manas, Sangworasil
    Classified Vector Quantization (CVQ) is used for coding images that achieves good perceptual results while reducing the computational load of the process. In this paper, image is sub-divided into 4×4 pixel blocks (vectors). Each vector is classified into an edge vector and a shade vector. Both edge vectors and shade vectors are used to design the codebooks of CVQ by Fuzzy C-Means (FCM) method. By doing so, the CVQ-FCM method can preserve the edge of image, make good image quality, and reduce the processing time while constructing the codebooks.