Title
Extension Theory for Classification of the Stored-Grain Insects
Abstract
The design of the classifier is one of the important parts of the online detection system of the stored-grain insects based on the image recognition technology. The classification of the insects was of many image feature parameters, and the mixing degree among feature parameters of various species of the insects was large. The extension theory was proposed to be applied to the automatic classification of the insects. A method that constructed the matter element matrix of the insects was put forward based on the mean and variance of the image features. After calculating the correlation degrees between the insect to be recognized and the nine species of insects, the insect could be recognized by the maximum integrated correlation degree criterion. The experiment confirms that the recognition of the insects based on the extension theory is practical and feasible by the training and analyzing of the samples of the insects.
Year
DOI
Venue
2010
10.1109/MVHI.2010.40
MVHI
Keywords
Field
DocType
automatic classification,matter-element matrix,stored-grain insects,correlation degree,extension theory,stored-grain insect,image feature,maximum integrated correlation degree,image feature parameter,feature parameter,classification,image recognition technology,various species,image features,pollution,set theory,water conservation,artificial neural networks,stability,machine vision,image recognition,feature extraction,image classification
Extension theory,Computer science,Matrix (mathematics),Artificial intelligence,Classifier (linguistics),Contextual image classification,Object detection,Computer vision,Pattern recognition,Feature (computer vision),Feature extraction,Matter element,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4244-6596-5
0
0.34
References 
Authors
1
2
Name
Order
Citations
PageRank
Hongtao Zhang110.70
Yuxia Hu200.68