Description: Image thresholding has played an important role in image segmentation. In this paper, we present a novel spatially weighted fuzzy c-means (SWFCM) clustering algorithm for image thresholding. The algorithm is formulated by incorporating the spatial neighborhood information into the standard FCM clustering algorithm. Two improved implementations of the k-nearest neighbor (k-NN) algorithm are introduced for calculating the weight in the SWFCM algorithm so as to improve the performance of image thresholding. Platform: |
Size: 293888 |
Author:silviudog |
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Description: Fuzzy c-means clustering (FCM) with spatial constraints (FCM_S) is an
effective algorithm suitable for image segmentation. Its effectiveness contributes not
only to introduction of fuzziness for belongingness of each pixel but also to
exploitation of spatial contextual information. Although the contextual information
can raise its insensitivity to noise to some extent, FCM_S (1) still lacks enough
robustness to noise and outliers and (2) is not suitable for revealing non-Euclidean
structure of the input data due to the use of Euclidean distance (L2 norm). Platform: |
Size: 36864 |
Author:mahsy |
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