Abstract
Object recognition is a type of pattern recognition. Object recognition is widely used in the manufacturing industry for the purpose of inspection. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing, and nanotechnology to multimedia databases. In this paper, mechanical objects recognition used in manufacturing industry is taken into consideration. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear, and variations in raw material. This paper considers the problem of recognising and classifying of such objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The features are extracted using Fourier descriptor technique. Artificial neural network (ANN) with back-propagation learning algorithm is used to train the network and for classification of five different objects. This paper shows the effect of learning rate and momentum on the classification accuracy of objects.
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