Abstract
Digital image segmentation based on Otsu's method is one of the most widely used technique for threshold selection. With Otsu's method, an optimum threshold is found by maximizing the between-class variance and the algorithm assumes that the image contains two classes of pixels or bi-modal histogram (e.g., foreground and background). It then calculates the optimal threshold value separating these two classes so that, their between class variance is maximal. The optimum threshold value is found by an exhaustive search among the full range of gray levels (e.g., 256 levels of intensity). The objective of this paper is to develop a fast algorithm for the Otsu method that reduces the number of search iterations. A new search technique is developed and compared with the original Otsu method. Experiments on several images show that the proposed Otsu-Checkpoints fast method give the same estimated threshold value with less number of iterations thus resulting in a much less computational complexity.