A modified fuzzy C-means image segmentation algorithm for use with uneven illumination patterns

被引:87
作者
Ma, Li [1 ]
Staunton, R. C. [1 ]
机构
[1] Univ Warwick, Sch Engn, Warwick, England
关键词
fuzzy clustering; image segmentation; biased illumination field; illumination pattern; projected pattern;
D O I
10.1016/j.patcog.2007.02.005
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A novel fuzzy C-mean (FCM) algorithm is proposed for use when active or structured light patterns are projected onto a scene. The underlying inhomogeneous illumination intensity due to the point source nature of the projection, surface orientation and curvature has been estimated and its effect on the object segmentation minimized. Firstly, we modified the recursive FCM algorithm to include biased illumination field estimation. New clustering center and fuzzy clustering functions resulted based on the intensity and average intensity of a pixel neighborhood based object function. Finally, a dilation operator was used on the initial segmented image for further refinement. Experimental results showed the proposed method was effective for segmenting images illuminated by patterns containing underlying biased intensity fields. A higher accuracy was obtained than for traditional FCM and thresholding techniques. (c) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:3005 / 3011
页数:7
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