Share Email Print
cover

Proceedings Paper

Research on the recognition of chironomid larvae based on SVM
Author(s): Jingying Zhao; Hai Guo; Xing-bin Sun
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

The traditional method of detecting Chironomid larvaes and plankton in water mostly is observation by Naked Eye, which is inefficient and inaccurate. This paper puts forward the Chironomid larvae image recognition method which is based on the support vector machines and multi-layered wavelet decomposition. Gradation histogram balance strengthening treatment is carried out for the image, so as to improve the contrast ratio and make for the threshold division. For each image, a 36 dimension feature vector is computed via two-level discrete Wavelet transform (DWT). The last step of the proposed approach consists is using support vector machine(SVM) as classifer and Wavelet energy as features to recognize the images. Extensive classification experiments on our image data validate that it is promising to employ the proposed texture features to recognize Chironomid larvaes in image.

Paper Details

Date Published: 10 July 2009
PDF: 5 pages
Proc. SPIE 7489, PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering, 74891D (10 July 2009); doi: 10.1117/12.836704
Show Author Affiliations
Jingying Zhao, Dalian Nationalities Univ. (China)
Hai Guo, Dalian Nationalities Univ. (China)
Xing-bin Sun, Northeast Forestry Univ. (China)


Published in SPIE Proceedings Vol. 7489:
PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering
Honghua Tan; Qi Luo, Editor(s)

© SPIE. Terms of Use
Back to Top