@article {10.3844/ajeassp.2019.413.419,
article_type = {journal},
title = {Histogram Matching Schemes for Image Thresholding},
author = {Ameer, Salah},
volume = {12},
number = {3},
year = {2019},
month = {Sep},
pages = {413-419},
doi = {10.3844/ajeassp.2019.413.419},
url = {https://thescipub.com/abstract/ajeassp.2019.413.419},
abstract = {This paper proposes several novel schemes for image thresholding. The idea is simply to compare the original image histogram to that of the thresholded image. Element by element comparison (sum of absolute difference between the two histograms) is found to be of better performance than a single feature (area or size) comparison. The optimum threshold is the one producing the best comparison. Cumulative histogram is introduced as a generalization to the area under the curve and found to be of better performance. In addition, a new performance measure is suggested based on percentage of correct assignments in both foreground and background. Comparative results with Otsu shows the effectiveness of the proposed schemes.},
journal = {American Journal of Engineering and Applied Sciences},
publisher = {Science Publications}
}