Fuzzy-C-means clustering based on the gray and spatial feature for image segmentation | |
Li, Ming; Li, Yun-song | |
2006 | |
页码 | 1641-1646 |
英文摘要 | Fuzzy c-means (FCM) clustering algorithm has been widely used in automated image segmentation. However, the standard FCM algorithm is sensitive to noise because of taking no into account the gray and spatial information of pixel. The paper proposes an improved FCM algorithm for image segmentation. We use the degree of gray similarity and distribution statistics of the neighbor pixels to form a new membership function for clustering. Not only it is effective to remove the noise spots and reduce the spurious blobs, but also it is ease to correct the misclassified pixels. Experimental results on three types of image indicate that the propose algorithm is more accurate and robust than the standard FCM algorithm. |
会议录 | 2006 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY, PTS 1 AND 2, PROCEEDINGS
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会议录出版者 | IEEE |
会议录出版地 | 345 E 47TH ST, NEW YORK, NY 10017 USA |
语种 | 英语 |
WOS研究方向 | Computer Science |
WOS记录号 | WOS:000243679800350 |
内容类型 | 会议论文 |
源URL | [http://119.78.100.223/handle/2XXMBERH/38259] ![]() |
专题 | 兰州理工大学 |
通讯作者 | Li, Ming |
作者单位 | Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Peoples R China |
推荐引用方式 GB/T 7714 | Li, Ming,Li, Yun-song. Fuzzy-C-means clustering based on the gray and spatial feature for image segmentation[C]. 见:. |
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