Image Bi-Level Thresholding Based on Gray Level-Local Variance Histogram
<p>Gray level local variance (GLLV) histogram.</p> "> Figure 2
<p>The testing images, their ground-truth images, and their variance images. (<b>a</b>) <span class="html-italic">Blood</span>; (<b>b</b>) <span class="html-italic">Bacteria</span>; (<b>c</b>) <span class="html-italic">Ant</span>; (<b>d</b>) <span class="html-italic">Stele</span>; (<b>e</b>) <span class="html-italic">Bird</span>; (<b>f</b>) <span class="html-italic">Factory</span>; (<b>g</b>) <span class="html-italic">Ceram</span>; (<b>h</b>) <span class="html-italic">Boat</span>.</p> "> Figure 3
<p>Thresholding results of test images using different methods. From left to right, the results are obtained by 1D KSW (Kapur’s 1D entropic method), 2D KSW (Abutaleb’s 2D approach), GLSC KSW (Xiao’s GLSC proposition), GLGM KSW (Xiao’s GLGM proposition), and our approach.</p> ">
Abstract
:1. Introduction
2. GLLV Histogram
2.1. Local Feature of Image via Local Variance
2.2. Construction of GLLV Histogram
3. Image Thresholding Based on GLLV
4. Experimental Results and Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Shao, J.X.; Dong, D.; Baohua, C.; Han, S. Automatic weld defect detection based on potential defect tracking in real-time radiographic image sequence. NDT & E Int. 2012, 46, 14–21. [Google Scholar]
- Tsneg, Y.H.; Tsai, D.M. Defect detection of uneven brightness in low-contrast images using basis image representation. Pattern Recognit. 2010, 43, 1129–1141. [Google Scholar] [CrossRef]
- Namane, A.; Guessoum, A.; Soubari, E.; Meyrueis, P. CSM neural network for degraded printed character optical recognition. J. Vis. Commun. Image Represent. 2014, 25, 1171–1186. [Google Scholar] [CrossRef]
- Ramírez-Ortegón, M.A.; Ramírez-Ramírez, L.L.; Märgner, V.; Messaoud, I.B.; Cuevas, E.; Rojas, R. An analysis of the transition proportion for binarization in handwritten historical documents. Pattern Recognit. 2014, 47, 2635–2651. [Google Scholar] [CrossRef]
- Gonçalves, H.; Gonçalves, J.A.; Corte-Real, L. HAIRIS: A method for automatic image registration through histogram-based image segmentation. IEEE Trans. Image Process. 2011, 20, 776–789. [Google Scholar] [CrossRef] [PubMed]
- Otsu, N. A threshold selection method from gray-level histogram. IEEE Trans. Syst. Man Cybern. 1979, 9, 62–66. [Google Scholar] [CrossRef]
- Kittler, J.; Illingworth, J. Minimum error thresholding. Pattern Recognit. 1986, 19, 41–47. [Google Scholar] [CrossRef]
- Pun, T. A new method for grey-level picture thresholding using the entropy of the histogram. Signal Process. 1980, 2, 223–227. [Google Scholar] [CrossRef]
- Pun, T. Entropic thresholding: A new approach. Comput. Vis. Graph. Image Process. 1981, 16, 210–239. [Google Scholar] [CrossRef]
- Wong, K.C.; Sahoo, P.K. A gray-level threshold selection method based on maximum entropy principle. IEEE Trans. Syst. Man Cybern. 1989, 19, 866–871. [Google Scholar] [CrossRef]
- Kapur, J.N.; Sahoo, P.K.; Wong, A.K.C. A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vis. Graph. Image Process. 1985, 29, 273–285. [Google Scholar] [CrossRef]
- Li, C.H.; Lee, C.K. Minimum cross entropy thresholding. Pattern Recognit. 1993, 26, 617–625. [Google Scholar] [CrossRef]
- Abutaleb, A. Automatic thresholding of gray-level pictures using two-dimensional entropy. Comput. Vis. Graph. Image Process. 1989, 47, 22–32. [Google Scholar] [CrossRef]
- Sahoo, P.K.; Arora, G. A thrsholding methd based on two-dimensional Renyi’s entropy. Pattern Recognit. 2004, 37, 1149–1161. [Google Scholar] [CrossRef]
- Liu, J.Z.; Li, W.Q. The automatic threshold of gray 2 level pictures via two 2 dimensional otsu method. Autom. Sin. 1993, 19, 101–105. (In Chinese) [Google Scholar]
- Tang, Y.; Di, Q.; Zhao, L.; Guan, X.; Liu, F. Image thesholding segmentation based on two-dimensional minimum Tsallis cross entropy. Acta Phys. Sin. 2009, 58, 9–15. [Google Scholar]
- Sahoo, P.K.; Arora, G. Image thresholding using two-dimensional Tsallis-Havrda-Charvat entropy. Pattern Recognit. Lett. 2006, 27, 520–528. [Google Scholar] [CrossRef]
- Xiao, Y.; Cao, Z.; Zhang, T. Entropic thresholding based on gray-level spatial correlation histogram. In Proceedings of the 19th International Conference on Pattern Recognition, Tampa, FL, USA, 8–11 December 2008; pp. 1–4. [Google Scholar]
- Yimit, A.; Hagihara, Y.; Miyoshi, T.; Hagihara, Y. 2-D direction histogram based entropic thresholding. Neurocomputing 2013, 120, 287–297. [Google Scholar] [CrossRef]
- Xiao, Y.; Cao, Z.; Yuan, J. Entropic image thresholding based on GLGM histogram. Pattern Recognit. Lett. 2014, 40, 47–55. [Google Scholar] [CrossRef]
- Wang, Z.J.; Sheng, H.Y. An Approach of Grad-based Image Segmentation. Appl. Res. Comput. 2004, 2, 254–258. [Google Scholar]
- De Albuquerque, M.P.; Esquef, I.A.; Mello, A.R.G. Image thresholding using Tsallis entropy. Pattern Recognit. Lett. 2004, 25, 1059–1065. [Google Scholar] [CrossRef]
- Berkely Image Segmentation Dataset. Available online: https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/ (accessed on 26 April 2017).
- Huang, D.; Wang, C. Optimal multi-level thresholding using a two-stage Otsu optimization appoach. Pattern Recognit. Lett. 2009, 30, 275–284. [Google Scholar] [CrossRef]
- Li, Z.; Liu, C.; Liu, G.; Cheng, Y.; Yang, X.; Zhao, C. A novel statistical image thresholding method. Int. J. Electron. Commun. 2010, 64, 1137–1147. [Google Scholar] [CrossRef]
Image | 1D KSW | 2D KSW | GLSC KSW | GLGM KSW | Our Method |
---|---|---|---|---|---|
Blood | 0.5120 | 0.1059 | 0.3487 | 0.4655 | 0.0176 |
Ant | 0.0909 | 0.0839 | 0.1175 | 0.0817 | 0.0684 |
Stele | 0.0475 | 0.0069 | 0.0070 | 0.0057 | 0.0053 |
Ceram | 0.1164 | 0.2786 | 0.5044 | 0.5136 | 0.1100 |
Bird | 0.1501 | 0.1508 | 0.0992 | 0.0802 | 0.0740 |
Bacteria | 0.0152 | 0.0128 | 0.0059 | 0.0058 | 0.0057 |
Boat | 0.0145 | 0.0297 | 0.2065 | 0.1063 | 0.0129 |
Factory | 0.0204 | 0.0877 | 0.1730 | 0.1730 | 0.0186 |
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zheng, X.; Ye, H.; Tang, Y. Image Bi-Level Thresholding Based on Gray Level-Local Variance Histogram. Entropy 2017, 19, 191. https://doi.org/10.3390/e19050191
Zheng X, Ye H, Tang Y. Image Bi-Level Thresholding Based on Gray Level-Local Variance Histogram. Entropy. 2017; 19(5):191. https://doi.org/10.3390/e19050191
Chicago/Turabian StyleZheng, Xiulian, Hong Ye, and Yinggan Tang. 2017. "Image Bi-Level Thresholding Based on Gray Level-Local Variance Histogram" Entropy 19, no. 5: 191. https://doi.org/10.3390/e19050191