{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/kernelized-weighted-susan-based-fuzzy-c-means","title":"Kernelized Weighted SUSAN based Fuzzy C-Means Clustering for Noisy Image Segmentation","arxiv_id":"1603.08564","date":"2016-03-28","proceeding":null,"authors":["Satrajit Mukherjee","Bodhisattwa Prasad Majumder","Aritran Piplai","Swagatam Das"],"abstract":"The paper proposes a novel Kernelized image segmentation scheme for noisy\nimages that utilizes the concept of Smallest Univalue Segment Assimilating\nNucleus (SUSAN) and incorporates spatial constraints by computing circular\ncolour map induced weights. Fuzzy damping coefficients are obtained for each\nnucleus or center pixel on the basis of the corresponding weighted SUSAN area\nvalues, the weights being equal to the inverse of the number of horizontal and\nvertical moves required to reach a neighborhood pixel from the center pixel.\nThese weights are used to vary the contributions of the different nuclei in the\nKernel based framework. The paper also presents an edge quality metric obtained\nby fuzzy decision based edge candidate selection and final computation of the\nblurriness of the edges after their selection. The inability of existing\nalgorithms to preserve edge information and structural details in their\nsegmented maps necessitates the computation of the edge quality factor (EQF)\nfor all the competing algorithms. Qualitative and quantitative analysis have\nbeen rendered with respect to state-of-the-art algorithms and for images ridden\nwith varying types of noises. Speckle noise ridden SAR images and Rician noise\nridden Magnetic Resonance Images have also been considered for evaluating the\neffectiveness of the proposed algorithm in extracting important segmentation\ninformation.","url_abs":"http://arxiv.org/abs/1603.08564v1","url_pdf":"http://arxiv.org/pdf/1603.08564v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"kernelized-weighted-susan-based-fuzzy-c-means","repo_url":"https://github.com/majumderb/TheFaultEngine","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}