A Computational Improvement of Otsu's Algorithm by Estimating Approximate Threshold

Abstract

There are various algorithms evaluating a threshold for image segmentation. Among them, Otsu's algorithm sets a threshold based on the histogram. It finds the between-class variance for all over gray levels and then sets the largest one as Otsu's optimal threshold, so we can see that Otsu's algorithm requires a lot of the computation. In this paper, we improved the amount of computational needs by using estimated Otsu's threshold rather than computing for all the threshold candidates. The proposed algorithm is compared with the original one in computation amount and accuracy. we confirm that the proposed algorithm is about 29 times faster than conventional method on single processor and about 4 times faster than on parallel processing architecture machine.

Map Space Modeling Method Reflecting Safety Margin in Coastal Water Based on Electronic Chart for Path Planning

“…Otsu’s binarization algorithm [ 21 ] was performed on the 2D grayscale image to create a binarized image divided into black and white. Otsu’s algorithm divides pixels into two classes by randomly setting a threshold value, repeats the task of obtaining the intensity distribution of the two classes, and selects the threshold value when the intensity distribution of the two classes is the most uniform among all cases.…”