%0 Conference Proceedings
%T Shape Feature Extraction of Wheat Leaf Disease Based on Invariant Moment Theory
%+ Zhengzhou University of Light Industry
%A Diao, Zhihua
%A Zheng, Anping
%A Wu, Yuanyuan
%Z Part 1: GIS, GPS, RS and Precision Farming
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 5th Computer and Computing Technologies in Agriculture (CCTA)
%C Beijing, China
%Y Daoliang Li
%Y Yingyi Chen
%I Springer
%3 Computer and Computing Technologies in Agriculture V
%V AICT-369
%P 168-173
%8 2011-10-29
%D 2011
%R 10.1007/978-3-642-27278-3_18
%K feature extraction
%K shape feature
%K invariant moment
%K wheat disease
%Z Computer Science [cs]Conference papers
%X Shape feature extraction is a key research direction on wheat leaf disease recognition. In order to resolve the problem of translation, scaling and rotation transformation invariance on shape matching, the invariant moment theory was introduced to shape feature extraction and seven Hu invariant moment parameters were defined as shape features. Meanwhile the present algorithm was used and new parameters were defined for shape feature extraction research on wheat leaf disease image. The shape features suitable for two types of wheat leaf disease recognition were received and applied in wheat disease intelligent recognition system. The results show that the system recognition rate is relatively high, and can meet the practical application requirements.
%G English
%Z TC 5
%Z WG 5.14
%2 https://inria.hal.science/hal-01360977/document
%2 https://inria.hal.science/hal-01360977/file/978-3-642-27278-3_18_Chapter.pdf
%L hal-01360977
%U https://inria.hal.science/hal-01360977
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG5-14
%~ IFIP-CCTA
%~ IFIP-AICT-369