Novel Image Correction Method Based on Swarm Intelligence Approach

1. Aydin, D.: An efficient ant-based edge detector. T. Comput. Collective Intell. 1, 39–55 (2010)

   Google Scholar 

2. Benatcha, K., Koudil, M., Benkhelat, N., Boukir, Y.: ISA an algorithm for image segmentation using ants. In: Proceedings of IEEE International Symposium on Industrial Electronics, pp. 2503–2507 (2008)

   Google Scholar 

3. Bhandari, A., Singh, V., Kumar, A., Singh, G.: Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using kapurs entropy. Expert Syst. Appl. 41(7), 3538–3560 (2014)

   Article  Google Scholar 

4. Brociek, R., Słota, D.: Reconstruction of the boundary condition for the heat conduction equation of fractional order. Thermal Sci. 19, 35–42 (2015)

   Article  Google Scholar 

5. Brociek, R., Słota, D.: Application of intelligent algorithm to solve the fractional heat conduction inverse problem. Commun. Comput. Inf. Sci. 538, 356–365 (2015)

   Article  Google Scholar 

6. Budnikas, G.: A model for an aggression discovery through person online behavior. In: Saeed, K., Homenda, W. (eds.) CISIM 2015. LNCS, vol. 9339, pp. 305–315. Springer, Heidelberg (2015). doi:10.1007/978-3-319-24369-6_25

   Chapter  Google Scholar 

7. Cpałka, K., Łapa, K., Przybył, A.: A new approach to design of control systems using genetic programming. Inf. Technol. Control 44(4), 433–442 (2015)

   Google Scholar 

8. Damaševičius, R.: Structural analysis of regulatory DNA sequences using grammar inference and support vector machine. Neurocomputing 73(4–6), 633–638 (2010)

   Google Scholar 

9. Ferdowsi, S., Voloshynovskiy, S., Kostadinov, D., Korytkowski, M., Scherer, R.: Secure representation of images using multi-layer compression. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.). LNCS, vol. 9119, pp. 696–705. Springer, Heidelberg (2015)

   Chapter  Google Scholar 

10. Hetmaniok, E., Słota, D., Zielonka, A.: Experimental verification of immune recruitment mechanism and clonal selection algorithm applied for solving the inverse problems of pure metal solidification. Int. Commun. Heat Mass Transf. 47, 7–14 (2013)

    Article  Google Scholar 

11. Korytkowski, M., Rutkowski, L., Scherer, R.: Fast image classification by boosting fuzzy classifiers. Inf. Sci. 327, 175–182 (2016)

    Article  MathSciNet  Google Scholar 

12. Keshtkar, F., Gueaieb, W.: Segmentation of dental radiographs using a swarm intelligence approach. In: Proceedings of Canadian Conference Electrical and Computer Engineering, pp. 328–331 (2006)

    Google Scholar 

13. Lakehal, E.: A swarm intelligence based approach for image feature extraction. In: Proceedings of International Conference on Multimedia Computing and Systems, pp. 31–35 (2009)

    Google Scholar 

14. Napoli, C., Pappalardo, G., Tramontana, E.: A mathematical model for file fragment diffusion and a neural predictor to manage priority queues over BitTorrent. Appl. Math. Comput. Sci. 26(1), 147–160 (2016)

    MathSciNet  MATH  Google Scholar 

15. Napoli, C., Pappalardo, G., Tramontana, E., Zappalà, G.: A cloud-distributed GPU architecture for pattern identification in segmented detectors big-data surveys. Comput. J. 59(3), 338–352 (2016)

    Article  Google Scholar 

16. Mishra, A., Agarwal, C., Sharma, A., Bedi, P.: Optimized gray-scale image water- marking using DWT SVD and firefly algorithm. Expert Syst. Appl. 41(17), 7858–7867 (2014)

    Article  Google Scholar 

17. Okulewicz, M., Mandziuk, J.: Two-phase multi-swarm PSO and the dynamic vehicle routing problem. In: Proceedings of the IEEE Symposium Series on Computational Intelligence, pp. 86–93 (2014)

    Google Scholar 

18. Ouadfel, S., Batouche, M.: MRF-based image segmentation using ant colony system. Electron. Lett. Comput. Vis. Image Anal. 2(2), 12–24 (2013)

    Google Scholar 

19. Pranevicius, H., Kraujalis, T., Budnikas, G., Pilkauskas, V.: Fuzzy rule base generation using discretization of membership functions and neural network. Commun. Comput. Inf. Sci. 465, 160–171 (2014)

    Article  Google Scholar 

20. Stateczny, A., Wlodarczyk-Sielicka, M.: Self-organizing artificial neural networks into hydrographic big data reduction process. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds.) RSEISP 2014. LNCS, vol. 8537, pp. 335–342. Springer, Heidelberg (2014)

    Google Scholar 

21. Swiechowski, M., Mandziuk, J.: Self-adaptation of playing strategies in general game playing. IEEE Trans. Comput. Intell. AI Games 6(4), 367–381 (2014)

    Article  Google Scholar 

22. Tian, J., Yu, W., Chen, L., Ma, L.: Image edge detection using variation-adaptive ant colony optimization. In: Nguyen, N.T. (ed.) Transactions on Computational Collective Intelligence V. LNCS, vol. 6910, pp. 27–40. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

23. Waledzik, K., Mandziuk, J.: An automatically generated evaluation function in general game playing. IEEE Trans. Comput. Intell. AI Games 6(3), 258–270 (2014)

    Article  Google Scholar 

24. Wang, Y., Wan, Q.: Detecting moving objects by ant colony system in a MAP-MRF framework. In: Proceedings of International Conference on E-Product E-Service and E-Entertainment, pp. 1–4 (2010)

    Google Scholar 

25. Wlodarczyk-Sielicka, M., Stateczny, A.: Selection of SOM parameters for the needs of clusterisation of data obtained by interferometric methods. In: Proceedings of 16th International Radar Symposium, Dresden, pp. 1129–1134 (2015)

    Google Scholar 

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