Abstract
In thresholded image segmentation, correct and adequate extraction of pixel distribution information is the key. In this paper, asymmetrical gray transition co-occurrence matrix is applied to better represent the spatial distribution information of images, and uniformity probability of binarization image is introduced to calculated the deviation information between original and thresholding image. A novel minimum square distance criterion function is proposed to select threshold value, and the vector correlation coefficient is deduced to interpret the reasonable of new criterion. Comparing with relative entropy method, the proposed method is simpler, moreover, it has outstanding object extraction performance.
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References
Sang, Q., Lin, Z.L., Acton, S.T.: Learning automata for image segmentation. Pattern Recognit. Lett. 74, 46–52 (2016). https://doi.org/10.1016/j.patrec.2015.12.004
Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 146–168 (2004). https://doi.org/10.1117/1.1631315
Chanda, B., Chaudhuri, B.B., Majumder, D.D.: On image enhancement and threshold selection using the gray-level co-occurrence matrix. Pattern Recognit. Lett. 3(2), 243–251 (1985). https://doi.org/10.1016/0167-8655(85)90004-2
Liang, D., Kaneko, S., Hashimoto, M., et al.: Co-occurrence probability-based pixel pairs background model for robust object detection in dynamic scenes. Pattern Recognit. 48, 1370–1386 (2015). https://doi.org/10.1016/j.patcog.2014.10.020
El-Feghi, I., Adem, N., Sid-Ahmed, M.A., et al.: Improved co-occurrence matrix as a feature space for relative entropy-based image thresholding. In: Proceedings of the Computer Graphics, Imaging and Visualization, vol. 49, pp. 314–320 (2007). http://doi.org/10.1109/CGIV.2007.49
Fan, J.L., Ren, J.: Symmetric co-occurrence matrix thresholding method based on square distance. Acta Electronica Sinica 39(10), 2277–2281 (2011). http://en.cnki.com.cn/Article_en/CJFDTOTAL-DZXU201110012.htm. (in Chinese)
Fan, J.L., Zhang, H.: A unique relative entropy-based symmetrical co-occurrence matrix thresholding with statistical spatial information. Chin. J. Electron. 24(3), 622–626 (2015). https://doi.org/10.1049/cje.2015.07.031
Subudhi, P., Mukhopadhyay, S.: A novel texture segmentation method based on co-occurrence energy-driven parametric active contour model. Signal, Image Video Process. 12(4), 669–676 (2018). https://doi.org/10.1007/s11760-017-1206-4
Pal, S.K., Pal, N.R.: Segmentation using contrast and homogeneity measure. Patt. Recog. Lett. 5, 293–304 (1987). https://doi.org/10.1016/0167-8655(87)90061-4
Ramac, L.C., Varshney, P.K.: Image thresholding based on ali-silvey distance measures. Pattern Recognit. 30(7), 1161–1174 (1997). https://doi.org/10.1016/S0031-3203(96)00149-5
Chang, C.I., Chen, K., Wang, J., Althouse, M.L.G.: A relative entropy-based approach to image thresholding. Pattern Recognit. 27(9), 1275–1289 (1994). https://doi.org/10.1016/0031-3203(94)90011-6
Lee, S.H., Hong, S.J., Tsai, H.R.: Entropy thresholding and its parallel algorithm on the reconfigurable array of processors with wider bus networks. IEEE Trans. Image Proc. 8(9), 1242–1299 (1999). https://doi.org/10.1109/83.784435
Acknowledgments
This work is supported by the National Science Foundation of China (No. 61571361, 61671377), and the Science Plan Foundation of the Education Bureau of Shaanxi Province (No. 15JK1682).
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Zhang, H., Zhi, Q., Yang, F., Fan, J. (2019). Minimum Square Distance Thresholding Based on Asymmetrical Co-occurrence Matrix. In: Krömer, P., Zhang, H., Liang, Y., Pan, JS. (eds) Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications. ECC 2018. Advances in Intelligent Systems and Computing, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-03766-6_91
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DOI: https://doi.org/10.1007/978-3-030-03766-6_91
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