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Modified bacterial foraging algorithm based multilevel thresholding for image segmentation. Journal Engineering Applications of Artificial Intelligence, 24(4), 595–615. , & Sankur, B. (2004). Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronics Imaging, 13(1), 146–168. , & Osadciw, L. (2003). Optimization using particle swarm with near neighbor interactions. Lecture Notes Computer Science, vol. 2723, Springer Verlag, Berlin. , Proceedings of the 2000 IEEE International Conference on Robotics, 1999).

It is easy to conclude that by increasing the level of segmentation, the segmented image includes more detail. Therefore, the 6-level segmented image in different test cases is smoother than the 3-level one. As a conclusion, FODPSO-based image segmentation is able to reach a slightly better fitness solution in less CPU processing time than its alternatives (DPSO and PSO). 2 Experimental Results 37 Fig. 1 The result of segmentation with 2, 3, 4, and 5 thresholds, respectively (from left to right).

2014a). Multilevel image segmentation based on fractional-order darwinian particle swarm optimization. IEEE Transactions on Geoscience and Remote Sensing, 52(5), 2382–2394. , & Couceiro, M. , & Benediktsson, J. A. (2014b). Integration of segmentation techniques for classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters, 11(1), pp. 342–346. , Couceiro, M. , & Benediktsson, J. A. (2015a). A Novel Evolutionary Swarm Fuzzy Clustering Approach for Hyperspectral Imagery. IEEE Journal of Selected.

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CAS-CERN Accelerator School - Applied Geodesy for Particle Accelerators

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