NDLI: A hybrid feature extraction method-based object recognition by neural network

Content Provider	IEEE Xplore Digital Library
Author	Wahi, A. Athiq, F.M. Palanisamy, C.
Copyright Year	2008
Description	Author affiliation: Dept. of Inf. Technol., Bannari Amman Inst. of Technol., Sathyamangalam (Wahi, A.; Athiq, F.M.; Palanisamy, C.)
Abstract	This paper presents a neural based approach for rotated object recognition in still black and white images. The system comprises two phases: extraction of information from edge of the objects known as feature extraction and classification of objects by artificial neural network (ANN) trained with back propagation algorithm. The two different methods of features extraction are adopted from rotated edge images of the objects. First the magnitude of 2D discrete Fourier transform (DFT) of the rotated edge image of the object are computed and stored in a matrix form. The features are calculated from these coefficients and stored in a vector form. The above feature extraction method is repeated to all rotated images of the objects. The second is an efficient hybrid features extraction method which combines the features from first method and the features from 3 - level decomposition of rotated edge image by 2D discrete wavelet transform (DWT). The same method is followed to extract the features from all the rotated images. The neural classifiers are trained with 75% of data sets obtained by two different feature data sets. The performance of the neural systems is evaluated on 25% of test data sets. The results are compared in both cases which predict that ANN presents higher accuracy of object recognition rate when trained with method 2 data sets than method 1.
Starting Page	1
Ending Page	5
File Size	2627119
Page Count	5
File Format	PDF
ISBN	9781424435944
DOI	10.1109/ICCCNET.2008.4787728
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-18
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	ANN Image converters Image processing Preprocessing Discrete Fourier transforms Artificial neural networks Discrete wavelet transforms Object recognition Data mining Edge Image Filters Object Recognition Neural networks Feature extraction
Content Type	Text
Resource Type	Article

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