A multi-level thresholding algorithm for threshold count and values identification based on dynamic programming

Multilevel image thresholding is a simple and efficient segmentation technique. Thresholding criteria such as Otsu and Kapur objective functions are extensively used in the literature. They are effective techniques but suffer from poor computational complexity. Thus, methods such as dynamic programming for exact optimization or metaheuristic algorithms for approximate optimization are applied to improve runtime. However, most of these algorithms take the count of thresholds as input. Hence, a novel input-less algorithm that can identify the count and values of thresholds simultaneously is proposed. The proposed method is then compared to state of the art methods to assess its efficiency and effectiveness.