期刊
PATTERN RECOGNITION LETTERS
卷 24, 期 16, 页码 2935-2941出版社
ELSEVIER
DOI: 10.1016/S0167-8655(03)00154-5
关键词
local entropy; transition region; thresholding; gradient; segmentation
Transition region based thresholding is a newly developed approach for image segmentation in recent years. Gradient-based transition region extraction methods (G-TREM) are greatly affected by noise. Local entropy in information theory represents the variance of local region and catches the natural properties of transition regions. In this paper, we present a novel local entropy-based transition region extraction method (LE-TREM), which effectively reduces the affects of noise. Experimental results demonstrate that LE-TREM significantly outperforms the conventional G-TREM. (C) 2003 Elsevier B.V. All rights reserved.
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