Thresholding-based segmentation revisited using mixtures of generalized Gaussian distributions

This paper presents a new approach to image-thresholding-based segmentation. It considerably improves existing methods by efficiently modeling non-Gaussian and multi-modal class-conditional distributions. The proposed approach seamlessly: 1) extends the Otsu's method to arbitrary numbers of thresholds and 2) extends the Kittler and Illingworth minimum error thresholding to non-Gaussian and multi-modal class-conditional data. We use the recently-proposed mixture of generalized Gaussian distributions (MoGG) modeling, which enables to efficiently represent heavy-tailed data, as well as multi-modal histograms with flat and sharply-shaped peaks. Experiments performed on synthetic data and real-world image segmentation show the performance of the proposed approach with comparison to recent state-of-the-art techniques.