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In this work an ignorance-based fuzzy clustering algorithm is presented. The algorithm is based on the entropy-based clustering algorithm proposed by Yao et al.. In our proposal, we calculate the total ignorance instead of using the entropy at each data point to select the data point as the first cluster center. The experimental results show that the ignorance-based clustering improves the data classification...
In this paper we propose a new density based clustering algorithm. As with other density based clustering algorithms our approach does not require the number of clusters as input. A modification of the Kuwahara filter, used in image processing, is used to generate a special density map in which the brightness of pixels is indicative of the density of the data points. A framework for clustering is...
In this paper, an unsupervised image segmentation algorithm is proposed, which combines spatial constraints with a kernel fuzzy c-means (KFCM) clustering algorithm. Conventional KFCM clustering segmentation algorithm does not incorporate the spatial context information of image, which makes it sensitive to the noise and intensity variations. In order to overcome the shortcomings, the contents of image...
Image segmentation algorithm based on fuzzy c-means clustering is an important algorithm in the image segmentation field. It has been used widely. However, it is not successfully to segment the noise image because the algorithm disregards of special constraint information. It only considers the gray information. Therefore, we proposed a weighed FCM algorithm based on Gaussian kernel function for image...
This system presents a methodology for watermarking by detection of cracks in images of old statues of temples and old paintings. The cracks are detected by thresholding the output of the morphological top-hat transform. Afterwards, the thin dark brush strokes, which have been misidentified as cracks are removed using a semi-automatic procedure based on region growing. The cracks are classified using...
This work is an intermediate step toward a fuzzy version of the iterative guided spectral class rejection (IGSCR) classification algorithm. IGSCR combines a clustering algorithm and a decision rule to produce multiple classifications. Although fuzzy versions of these algorithms are available, IGSCR can only evaluate hard clustering output. In an effort to move toward fuzzy classification output, this...
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