FCM Implementation for Image Segmentation using Fuzzy Clustering
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FCM implementation represents a widely adopted image segmentation technique utilizing fuzzy clustering principles. In image processing applications, FCM algorithm operates by analyzing image characteristics and pixel values to perform clustering, effectively partitioning images into distinct regions for enhanced analysis and processing. The core implementation involves iterative optimization of cluster centers through membership degree calculations, typically employing Euclidean distance metrics for pixel-to-cluster similarity assessment. Key functions include fcm() for cluster initialization, compute_membership() for fuzzy partition matrix generation, and update_centers() for centroid recalculation during convergence iterations. This approach enables handling of overlapping region boundaries through probabilistic membership assignments rather than hard binary classifications.
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