The Vagueness Measure: A New Interpretation and an Application to Image Thresholding

Abstract

Here, we introduce a new interpretation of the vagueness measure (which appeared in an earlier work) and an application for this approach. If the vagueness measure is computed for the distribution function of a given population, the value obtained gives a similar characteristic as the standard deviation of the population. Based on this property, a new global thresholding algorithm was developed that generalizes the idea of Otsu's optimality criterion by the means of continuous-valued logic. The performance of this method is compared with other commonly used algorithms to validate the usefulness of the proposed approach. Although the purpose of this algorithm is to threshold a grayscale image (which can be a useful step in the segmentation process of biological and medical images), it can be generalized for other tasks that require the separation of two or more populations, characterized by real values.