Feature Extraction Method for Wheat Diseases Based on Multi-fractal Spectrum

Authors
Feiyun Zhang
Corresponding Author
Feiyun Zhang
Available Online March 2013.
DOI
https://doi.org/10.2991/iccsee.2013.765How to use a DOI?
Keywords
Wheat diseases, Multi-wavelet transform, Multi-fractal spectrum, Feature extraction, shape feature
Abstract
Wheat diseases image noise was effectively removed using lifting scheme multi-wavelet transform and multi-fractal analysis, and then it used multi-fractal theory to segment diseases image and extract eight multi-fractal spectrum values as wheat shape feature of diseases. Experiments showed that the shape characteristic value of different wheat diseases had great difference, and the shape characteristic value of similar diseases had certain regularity. Therefore, it could extract shape characteristic value to recognize wheat diseases.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering
Part of series
Advances in Intelligent Systems Research
Publication Date
March 2013
ISBN
978-90-78677-61-1
ISSN
1951-6851
DOI
https://doi.org/10.2991/iccsee.2013.765How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Feiyun Zhang
PY  - 2013/03
DA  - 2013/03
TI  - Feature Extraction Method for Wheat Diseases Based on Multi-fractal Spectrum
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering
PB  - Atlantis Press
SP  - 3049
EP  - 3052
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.765
DO  - https://doi.org/10.2991/iccsee.2013.765
ID  - Zhang2013/03
ER  -