1. Dey, S., Bhattacharyya, S. and Maulik, U.Quantum behaved multi-objective PSO and ACO optimization for multi-level thresholding. In 2014 International Conference on Computational Intelligence and Communication Networks (pp. 242-246). IEEE, 2014. 2. Bhandari A.K., Kumar A., andSingh G.K.Tsallis entropy based MT for colored satellite image segmentation using evolutionary algorithms.Expert Systems with Applications, 42(22): 8707-8730, 2015 3. Otsu N.A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics,9(1), pp.62-66, 1979. 4. Kapur, J.N., Sahoo, P.K. and Wong, A.K.A new method for gray-level picture thresholding using the entropy of the histogram.Computer vision, graphics, and image processing, 29(3), pp.273-285, 1985. 5. Huang, L.K. and Wang, M.J.J. Image thresholding by minimizing the measures of fuzziness.Pattern recognition, 28(1), pp.41-51, 1995. 6. Qiao Y., Hu Q., Qian G., Luo S. and Nowinski W.L.Thresholding based on variance and intensity contrast.Pattern Recognition, 40(2), pp.596-608, 2007. 7. Li, X., Zhao, Z. and Cheng, H.D.Fuzzy entropy threshold approach to breast cancer detection.Information Sciences-Applications, 4(1), pp.49-56, 1995. 8. Li, C.H. and Tam, P.K.S. An iterative algorithm for minimum cross entropy thresholding.Pattern recognition letters, 19(8), pp.771-776, 1998. 9. Li, K. and Tan, Z.An improved flower pollination optimizer algorithm for multilevel image thresholding.IEEE access, 7, pp.165571-165582, 2019. 10. Li Y., Bai X., Jiao L. and Xue Y.Partitioned-cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation.Applied Soft Computing, 56, pp.345-356, 2017. 11. Kittler, J. and Illingworth, J., 1986. Minimum error thresholding. Pattern recognition,19(1), pp.41-47. 12. De Albuquerque, M.P., Esquef, I.A. and Mello, A.G. Image thresholding using Tsallis entropy. Pattern Recognition Letters,25(9), pp.1059-1065, 2004. 13. Zarezadeh, S. and Asadi, M.Results on residual Rényi entropy of order statistics and record values.Information Sciences, 180(21), pp.4195-4206, 2010. 14. Hammouche, K., Diaf, M. and Siarry, P.A comparative study of various meta-heuristic techniques applied to the multilevel thresholding problem. Engineering Applications of Artificial Intelligence,23(5), pp.676-688, 2010. 15. Alihodzic, A. and Tuba, M.Improved bat algorithm applied to multilevel image thresholding.The Scientific World Journal, 2014. 16. Sri Madhava Raja, N., Rajinikanth, V. and Latha, K. Otsu based optimal multilevel image thresholding using firefly algorithm.Modelling and Simulation in Engineering, 2014. 17. Oliva D., Cuevas E., Pajares G., Zaldivar D. and Perez-Cisneros, M. Multilevel thresholding segmentation based on harmony search optimization.Journal of Applied Mathematics, 2013. 18. Liu Y., Mu C., Kou W. and Liu J.Modified particle swarm optimization-based multilevel thresholding for image segmentation.Soft computing, 19(5), pp.1311-1327, 2015. 19. Liu Y., Liu J., Tian L. and Ma L.Hybrid artificial root foraging optimizer based multilevel threshold for image segmentation.Computational intelligence and neuroscience, 2016. 20. Agarwal P., Singh R., Kumar S. and Bhattacharya M.Social spider algorithm employed multi-level thresholding segmentation approach. In Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 2 (pp. 249-259). Springer, Cham, 2016. 21. Ouadfel, S. and Taleb-Ahmed, A. Social spiders optimization and flower pollination algorithm for multilevel image thresholding: a performance study.Expert Systems with Applications, 55, pp.566-584, 2016. 22. Chen K., Zhou Y., Zhang Z., Dai M., Chao Y. and Shi J.Multilevel image segmentation based on an improved firefly algorithm.Mathematical Problems in Engineering, 2016. 23. Cao L.L., Ding S., Fu X.W. and Chen L.Otsu multilevel thresholding segmentation based on quantum particle swarm optimisation algorithm.International Journal of Wireless and Mobile Computing, 10(3), pp.272-277, 2016. 24. Pal S.S., Kumar S., Kashyap M., Choudhary Y. and Bhattacharya M.Multi-level thresholding segmentation approach based on spider monkey optimization algorithm. In Proceedings of the second international conference on computer and communication technologies (pp. 273-287). Springer, New Delhi, 2016. 25. Panda R., Agrawal S., Samantaray L. and Abraham A.An evolutionary gray gradient algorithm for multilevel thresholding of brain MR images using soft computing techniques.Applied Soft Computing, 50, pp.94-108, 2017. 26. Abd El Aziz, M., Ewees, A.A. and Hassanien, A.E. Hybrid swarms optimization based image segmentation. In Hybrid soft computing for image segmentation (pp. 1-21). Springer, Cham, 2016. 27. Abd El Aziz, M., Ewees, A.A. and Hassanien, A.E. Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation.Expert Systems with Applications, 83, pp.242-256, 2017. 28. Abd Elaziz, M., Ewees, A.A. and Oliva, D. Hyper-heuristic method for multilevel thresholding image segmentation. Expert Systems with Applications, 146, p.113201, 2020. 29. Khairuzzaman A.K.M. and Chaudhury, S. Multilevel thresholding using grey wolf optimizer for image segmentation.Expert Systems with Applications, 86, pp.64-76, 2017. 30. Rao, R.V., Savsani, V.J. and Vakharia, D.P.Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems.Information sciences, 183(1), pp.1-15, 2012. 31. Rao, R.V., Savsani, V.J. and Balic, J.Teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems. Engineering Optimization,44(12), pp.1447-1462, 2012. 32. Venkata Rao, R. and Kalyankar, V.D. Parameter optimization of machining processes using a new optimization algorithm. Materials and Manufacturing Processes,27(9), pp.978-985, 2012. 33. Venkata Rao, R. and Patel, V. Multi-objective optimization of combined Brayton and inverse Brayton cycles using advanced optimization algorithms. Engineering Optimization,44(8), pp.965-983, 2012. |