Image segmentation using logit-t mixtures

This paper introduces a novel heavy-tailed and alternative t type distribution within a bounded interval, arising as a scale mixture of the logit-normal distribution. Characterized by its prominent heavy-tails, excess kurtosis, and bounded characteristics, the proposed model offers a versatile and fitting solution for various computer vision and pattern recognition challenges. The ECME algorithm is considered for the precise estimation of model parameters and its finite mixtures. To demonstrate the efficacy of our approach, we conduct experiments using authentic natural and medical images. Our numerical findings underscore the enhanced robustness and accuracy of the proposed model in image segmentation when compared to traditional mixture models.