The Presentation of a New Method for Image Distinction with Robot by Using Rough Fuzzy Sets and Rough Fuzzy Neural Network Classifier
Distinguishing different images by robots and classifying
them in distinct groups is an important issue in robot vision.
In this paper we want to propose a new method for
distinguishing images by robot via using Rough fuzzy sets’
decreases method and Rough fuzzy neural network
classifier. In this method, the image features like color,
texture and shape are excluded and the redundant features
are decreased by Rough fuzzy sets method. Then the Rough
fuzzy neural network classifier is educated by the use of
these decreased features. In next phase, the robot can
properly classify the images; it has not seen or the examined
images and put them in the correct group by the use of this
system. We have compared our proposed method with
Johnson decreased method, principal component analysis,
and Rough sets, and also we have compared our classifier
with the support vector- machine classifier, neural network
and K-nearest neighbor. Our tests’ bed is 1000 images of
the COREL image set in ten semantic groups.
Keywords: image Distinction, rough fuzzy sets, Rough Fuzzy Neural Network Classifier
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