Entropic image thresholding based on GLGM histogram

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• 作者：Yang Xiao ; Zhiguo Cao ; Junsong Yuan
• 关键词：Entropic image thresholding ; GLGM histogram ; Gray level spatial property ; Gradient magnitude
• 刊名：Pattern Recognition Letters
• 出版年：15 April, 2014
• 年：2014
• 卷：40
• 期：Complete
• 页码：47-55
• 全文大小：4152 K

文摘

We propose GLGM (gray-level & gradient-magnitude) histogram as a novel image histogram for thresholding. GLGM histogram explicitly captures the gray level occurrence probability and spatial distribution property simultaneously. Different from previous histograms that also consider the spatial information, GLGM histogram employs the Fibonacci quantized gradient magnitude to characterize spatial information effectively. In this paper, it is applied to entropic image thresholding. For threshold selection, we define a new spatial property weighting function to depict the roles played by different kinds of pixels. The experiments demonstrate the effectiveness and robustness of our thresholding approach, containing wide range comparisons with the well established thresholding methods.