A fast thresholding selection procedure for multimodal and unimodal histograms

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Abstract

In this paper, a simple and efficient histogram-based approach is presented for multi-level thresholding. It uses Gaussian kernel smoothing to detect peaks and valleys in a multimodal histogram, and uses a local maximum curvature method to detect points of discontinuity in a unimodal histogram. The computational time will decrease as the desired number of thresholding levels increases. The performance of the proposed algorithm is compared with those of the widely applied between-class variance and entropy methods.

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