Multi-level image thresholding using Otsu and chaotic bat algorithm-Reference-Cited by-同舟云学术

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Multi-level image thresholding using Otsu and chaotic bat algorithm

Published:2016-10-28 Issue:12 Volume:29 Page:1285-1307
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Satapathy Suresh Chandra,Sri Madhava Raja N.,Rajinikanth V.,Ashour Amira S.,Dey Nilanjan

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/article/10.1007/s00521-016-2645-5/fulltext.html

Reference64 articles.

1. Ghosh SK (2012) Digital image processing. Narosa Publishing House Pvt. Ltd., New Delhi

2. Ghamisi P, Couceiro MS, Martins FML, Benediktsson JA (2014) Multilevel image segmentation based on fractional-order Darwinian particle swarm optimization. IEEE Trans Geosci Remote Sens 52(5):2382–2394

3. Bhandari AK, Kumar A, Singh GK (2015) Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur’s, Otsu and Tsallis functions. Expert Syst Appl 42(3):1573–1601

4. Manickavasagam K, Sutha S, Kamalanand K (2014) An automated system based on 2d empirical mode decomposition and k-means clustering for classification of Plasmodium species in thin blood smear images. BMC Infect Dis 14(Suppl 3):P13. doi: 10.1186/1471-2334-14-S3-P13

5. Manickavasagam K, Sutha S, Kamalanand K (2014) development of systems for classification of different plasmodium species in thin blood smear microscopic images. J Adv Microsc Res 9(2):86–92

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