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  4. Vol 8, Issue 1, 2025

Brightness-based Threshold-Weighted OTSU Method for Image Segmentation

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DOI: 10.23977/jipta.2025.080102 | Downloads: 24 | Views: 657

Author(s)

Dewei Xu 1, Xuqiang Li 2, Xin Zhang 2

Affiliation(s)

1 National Innovation Institute of High-end Smart Appliances, Qingdao, Shandong, China
2 Qingdao Economic and Technological Development Zone Haier Water Heater Co., Ltd, Qingdao, Shandong, China

Corresponding Author

Dewei Xu

ABSTRACT

In order to solve the problem that the OTSU method is less effective for segmenting low brightness images, this paper proposes the brightness-based threshold-weighted OTSU (TW-OTSU) method. A threshold weighting coefficient of segmentation threshold based on the average gray leve1 of images is introduced, a mapping relationship between the weighting coefficient of segmentation threshold and the average gray level is created. It is taken as the final segmentation threshold that the segmentation threshold calculated by the OTSU method is weighted with the gray level value, which is corresponding to the peak in the foreground of an image gray level distribution as the final segmentation threshold. The experiments show that the TW-OTSU method improves not onlysolves the problem of poor the segmentation effect of OTSU method for low brightness images, but also the comprehensive performance advantage of the algorithm in terms of segmentation accuracy and running time is obvious.

KEYWORDS

TW-OTSU, Image Segmentation, Weighting Coefficient based on Gray Level

CITE THIS PAPER

Dewei Xu, Xuqiang Li, Xin Zhang, Brightness-based Threshold-Weighted OTSU Method for Image Segmentation. Journal of Image Processing Theory and Applications (2025) Vol. 8: 8-16. DOI: http://dx.doi.org/10.23977/jipta.2025.080102.

REFERENCES

[1] Gongal A, Amatya S, Karkee M, et al. Sensors and systems for fruit detection and localization: A review[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2015, 116: 8-19.
[2] Wang A C, Zhang W, Wei X H. A review on weed detection using ground-based machine vision and image processing techniques [J]. Computers And Electronics In Agriculture, 2019, 158: 226-240.
[3] Tang Z W, Wu Y X, Ieee. one image segmentation method based on Otsu and fuzzy theory seeking image segment threshold[J]. 2011 International Conference On Electronics, Communications And Control (ICECC), 2011: 2170-2173.
[4] Otsu N. A Threshold Selection Method from Gray-Level Histograms[J]. IEEE Transactions on Systems Man & Cybernetics, 2007, 9(1): 62-66.
[5] Yuan X, Martinez J F, Eckert M, et al. An Improved Otsu Threshold Segmentation Method for Underwater Simultaneous Localization and Mapping-Based Navigation[J]. SENSORS, 2016, 16(7).
[6] Zhan Y T, Zhang G Y. An Improved OTSU Algorithm Using Histogram Accumulation Moment for Ore Segmentation [J]. Symmetry-Basel, 2019, 11(3).
[7] Du H, Chen X B, Xi J T. An improved background segmentation algorithm for fringe projection profilometry based on Otsu method [J]. Optics Communications, 2019, 453.
[8] Ng H F. Automatic thresholding for defect detection [J]. Pattern Recognition Letters, 2004, 27(14): 1644-1649.
[9] Fan J L, Lei B. A modified valley-emphasis method for automatic thresholding[J]. PATTERN RECOGNITION LETTERS, 2012, 33(6): 703-708.
[10] Yuan X C, Wu L S, Peng Q J. An improved Otsu method using the weighted object variance for defect detection[J]. APPLIED SURFACE SCIENCE, 2015, 349: 472-484.
[11] Pun T. A new method for gray-level picture thresholding using the entropy of the histogram[J]. SIGNAL PROCESSING, 1985, 2(3): 223-237.
[12] Hoang D N. Detection of Surface Crack in Building Structures Using Image Processing Technique with an Improved Otsu Method for Image Thresholding[J]. Advances in Civil Engineering, 2018, 2018.
[13] Qiao N S, Sun P. Study of improved Otsu algorithm and its ration evaluation analysis for PCB photoelectric image segmentation [J]. OPTIK, 2014, 125(17): 4784-4787.
[14] Jianzhuang Liu, Wenqing Li. A two-dimensional Otsu automatic threshold segmentation method of grayscale images [J]. Journal of Automation, 1993, (01):101-105
[15] He Z Y, Sun L N. Surface defect detection method for glass substrate using improved Otsu segmentation[J]. APPLIED OPTICS, 2015, 54(33): 9823-9830.
[16] Xiao L Y, Ouyang H L, Fan C D. An improved Otsu method for threshold segmentation based on set mapping and trapezoid region intercept histogram [J]. OPTIK, 2019, 196.
[17] Yu Q, Hu Q, Qian G, et al. Thresholding based on variance and intensity contrast[J]. Pattern Recognition, 2007, 40(2): 596-608.
[18] Hou Z, Hu Q, Nowinski W L. On minimum variance thresholding [J]. Pattern Recognition Letters, 2006, 27(14): 1732-1743.
[19] Xu X Y, Xu S Z, Jin L H, et al. Characteristic analysis of Otsu threshold and its applications[J]. PATTERN RECOGNITION LETTERS, 2011, 32(7): 956-961.

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