Abstract
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.
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