A FAST THRESHOLDING SELECTION PROCEDURE FOR MULTIMODAL AND UNIMODAL HISTOGRAMS

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.