Affiliations:
Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
Correspondence:
[*]
Corresponding author. Wattana Jindaluang, Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand. E-mail: wjindaluang@gmail.com.
Abstract: A machine learning method is now considered capable of accurately segmenting images. However, one significant disadvantage of this strategy is that it requires a lengthy training phase and an extensive training dataset. This article uses an image segmentation by histogram thresholding approach that does not require training to overcome this difficulty. This article proposes straightforward and time-optimal algorithms, which are guaranteed by mathematical proofs. Furthermore, we experiment with the proposed algorithms using 100 images from a standard database. The results show that, while their performances are not significantly different, the two proposed methods are roughly 10 and 20 times faster than the most simple and optimal method, Brute Force. They also show that the proposed algorithms can deal with bimodal images and images with various shapes of the image histogram. Because our proposed algorithms are the most efficient and effective. As a result, they can be used for real-time segmentations and as a pre-processing approach for multiple object segmentation.
