Title
Shape Feature Extraction Of Wheat Leaf Disease Based On Invariant Moment Theory
Abstract
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.
Year
DOI
Venue
2011
10.1007/978-3-642-27278-3_18
COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE V, PT II
Keywords
Field
DocType
feature extraction, shape feature, invariant moment, wheat disease
Computer vision,Pattern recognition,Recognition system,Invariant (physics),Feature extraction,Artificial intelligence,Invariant (mathematics),Scaling,Mathematics,Rotation (mathematics)
Conference
Volume
Issue
ISSN
369
PART 2
1868-4238
Citations 
PageRank 
References 
1
0.36
2
Authors
3
Name
Order
Citations
PageRank
Zhihua Diao110.70
AnPing Zheng210.70
Yuanyuan Wu310.36