Abstract
- An efficient global-contour-guided binarization technique is proposed. First, a high-pass convolution processes the gray-level image to get its contour binary image. The gray-level image is then uniformly partition into a predetermined number of blocks to get the reference points. For each subimage centered at a reference point, the best threshold is obtained by maximizing the black-to-black hit value associated with the corresponding part of the contour binary image. The two-dimensional cubic-convolution function uses reference point thresholds to interpolate the threshold surface and to get the binary image. Adjusting the division in the gray-level image provides alternative binary images. Combining the binary images with varying divisions generates the compound binary image. Experimental results show that the compound binary image outperforms binary images generated by other adaptive binarization methods.
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