NDLI: Recognition of Objects by Supervised Neural Network Using Wavelet Features

Content Provider	IEEE Xplore Digital Library
Author	Wahi, A. Thirumurugan, E.
Copyright Year	2008
Description	Author affiliation: Dept. of Inf. Technol., Bannari Amman Inst. of Technol., Sathyamangalam (Wahi, A.; Thirumurugan, E.)
Abstract	This paper presents a simple and robust method for recognition of rotated objects by Feedforward Neural Classifier. Initially, the translation invariance is achieved after pre-processing the image. Fourier transform is then applied to each of the rotated binary edge images with 5 degrees interval. Then the 3-level Discrete Wavelet Transform (DWT) is applied to compress the Fourier coefficients. The resulting data are then divided into 16 equal non-overlapping blocks, from which the statistical descriptors are calculated and it includes mean, median, standard deviation, energy and entropy. The 80 descriptor values obtained for each rotated image are used to train the Artificial Neural Network. The simulation works are carried on the standard image dataset obtained from the Library of Amsterdam University [28]. The high classification accuracy of 97.85 % is achieved during testing.
Starting Page	56
Ending Page	61
File Size	243141
Page Count	6
File Format	PDF
ISBN	9780769532677
DOI	10.1109/ICETET.2008.68
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-16
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Shape Image edge detection Feedforward Neural Classifier Artificial neural networks Discrete Fourier Transform Feature extraction Pattern recognition Statistical descriptors Discrete Wavelet Transform Testing
Content Type	Text
Resource Type	Article

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