Méthode d'Otsu

In computer vision and , Otsu's method, named after Nobuyuki Otsu, is used to perform automatic image . In the simplest form, the algorithm returns a single intensity threshold that separate pixels into two classes, foreground and background. This threshold is determined by minimizing intra-class intensity variance, or equivalently, by maximizing inter-class variance. Otsu's method is a one-dimensional discrete analogue of Fisher's Discriminant Analysis, is related to Jenks optimization method, and is equivalent to a globally optimal k-means performed on the intensity histogram. The extension to multi-level thresholding was described in the original paper, and computationally efficient implementations have since been proposed. The algorithm exhaustively searches for the threshold that minimizes the intra-class variance, defined as a weighted sum of variances of the two classes: Weights and are the probabilities of the two classes separated by a threshold ,and and are variances of these two classes. The class probability is computed from the bins of the histogram: For 2 classes, minimizing the intra-class variance is equivalent to maximizing inter-class variance: which is expressed in terms of class probabilities and class means , where the class means , and are: The following relations can be easily verified: The class probabilities and class means can be computed iteratively. This idea yields an effective algorithm. Compute histogram and probabilities of each intensity level Set up initial and Step through all possible thresholds maximum intensity Update and Compute Desired threshold corresponds to the maximum histogramCounts is a 256-element histogram of a grayscale image different gray-levels (typical for 8-bit images). level is the threshold for the image (double). function level = otsu(histogramCounts) total = sum(histogramCounts); % total number of pixels in the image %% OTSU automatic thresholding top = 256; sumB = 0; wB = 0; maximum = 0.