Classifying Insects from SEM Images Based on Optimal Classifier Selection and D-S Evidence Theory-Reference-Cited by-同舟云学术

Classifying Insects from SEM Images Based on Optimal Classifier Selection and D-S Evidence Theory

Published:2016 Issue:11 Volume:E99.A Page:1971-1980
ISSN:0916-8508
Container-title:IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
language:en
Short-container-title:IEICE Trans. Fundamentals

Author:

OGAWA Takahiro¹,TAKAHASHI Akihiro¹,HASEYAMA Miki¹

Affiliation:

1. Graduate School of Information Science and Technology, Hokkaido University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Signal Processing

Link

https://www.jstage.jst.go.jp/article/transfun/E99.A/11/E99.A_1971/_pdf

Reference41 articles.

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3. [3] B. Bhushan, “Biomimetics: Lessons from nature-an overview,” Philosophical Trans. Royal Society A: Mathematical, Physical and Engineering Sciences, vol.367, no.1893, pp.1445-1486, 2009.

4. [4] H. Zhang, Q. Huo, and W. Ding, “The application of AdaBoost-neural network in storedproduct insect classification,” 2008 IEEE International Symposium on IT in Medicine and Education, pp.973-976, 2008.

5. [5] A. Lu, X. Hou, C.L. Liu, and X. Chen, “Insect species recognition using discriminative local soft coding,” 21st International Conference on Pattern Recognition, pp.1221-1224, 2012.

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