An Autoregressive Model Approach to Two-Dimensional Shape Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision, Graphics, and Image Processing
Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Introduction to artificial neural systems
Introduction to artificial neural systems
Improving the convergence of the back-propagation algorithm
Neural Networks
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Neural Networks and Speech Processing
Neural Networks and Speech Processing
On the Hough Technique for Curve Detection
IEEE Transactions on Computers
A simple method to derive bounds on the size and to train multilayer neural networks
IEEE Transactions on Neural Networks
Mineral identification using color spaces and artificial neural networks
Computers & Geosciences
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In this paper, a neural network using an optimal linear feature extraction scheme is proposed to recognize two-dimensional objects in an industrial environment. This approach consists of two stages. First, the procedures of determining the coefficients of normalized rapid descriptor (NRD) of unknown 2-D objects from their boundary are described. To speed up the learning process of the neural network, an optimal linear feature extraction technique is used to extract the principal components of these NRD coefficients. Then, these reduced components are utilized to train a feedforward neural network for object recognition. We compare recognition performance, network sizes, and training time for networks trained with both reduced and unreduced data. The experimental results show that a significant reduction in training time can be achieved without a sacrifice in classifier accuracy.