Image histogram thresholding using Gaussian kernel density estimation (English)

In this article, image histogram thresholding is carried out using the likelihood of a mixture of Gaussians. In the proposed approach, a probability density function (PDF) of the histogram is computed using Gaussian kernel density estimation in an iterative manner. The threshold is found by iteratively computing a mixture of Gaussians for the two clusters. This process is aborted when the current bin is assigned to a different cluster than its predecessor. The method does not envolve an exhaustive search. Visual examples of our segmentation versus Otsu´s thresholding method are presented.