Abstract
Fuzzy C-means is a universally admired technique used in image segmentation. Morphological reconstruction is employed as image reconstruction at a lowered observation resolution to remove noise from images, so that space complexity of FCM algorithm is reduced, image is morphologically reconstructed, and noise present in original image is also avoided. Secondly, filtering of membership function is employed to trim the membership function to reduce the complexity of FCM function. Adaptive frequency-domain filtering of membership function allows for FCM function to be simpler and faster than existing algorithm.
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Dautaniya, A.K., Sharma, V. (2020). High-Performance Fuzzy C-Means Image Clustering Based on Adaptive Frequency-Domain Filtering and Morphological Reconstruction. In: Pant, M., Sharma, T., Verma, O., Singla, R., Sikander, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1053. Springer, Singapore. https://doi.org/10.1007/978-981-15-0751-9_112
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DOI: https://doi.org/10.1007/978-981-15-0751-9_112
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