A multilevel thresholding algorithm for image segmentation based on barnacle mating optimization - Nurul Farhana, Mohd Fakri; Nor Farizan, Zakaria; Mohd Herwan, Sulaiman; Rohana, Abdul Karim; Nurul Wahidah, Arshad; Yasmin, Abdul Wahab (Institution of Engineering and Technology, 2022)
One of the most crucial topics in the study of image segmentation is multilevel thresholding. However, as the number of thresholds rises, the computing cost of multilayer thresholding grows exponentially. This paper proposes a novel multilevel thresholding method based on Barnacles Mating Optimization (BMO) to address this disadvantage. The way that barnacles naturally mate was a major source of inspiration for BMO. This bio-inspired approach is used to solve multilevel thresholding issues by applying Otsu's between-class variance and Kapur's entropy functions to determine the ideal threshold configuration. Using more than one threshold, multilevel thresholding separates pixels of the image into multiple classes that permit the analysis of the objects in the image. The results are compared with those of other methods found in the literature review.