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Vehicle Recognition System Using Singular Value Decomposition (SVD) and Levenberg-Marquardt

Authors: Zuraidi Saad; Muhammad Khusairi Osman; Zuli Imran Zulkafli; Sopiah Ishak;

Vehicle Recognition System Using Singular Value Decomposition (SVD) and Levenberg-Marquardt

Abstract

The purpose of this research is to develop a system that used to recognize image of vehicle and classified it into their classes using image processing method and artificial neural network. In the research, all the selected images are required to go through image processing technique to obtained desired data. Images are converted into data using singular value decomposition extraction method and the data then are used as the input for the training purposes. The multilayered perceptron network trained by Levenberg-Marquardt algorithm was chosen in recognition and classification stage. The input variables were taken from 3 sets images of motorcycle, bus and lorry. The data inputs consist of 215 data. For training data set is 96 sets of data and used in training process and the other used in testing process. This training method can recognize the vehicle type in images successfully.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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