Abstract
In this work, a simplified Artificial Neural Network (ANN) based approach for recognition of various objects is explored using multiple features. The objective is to configure and train an ANN to be capable of recognizing an object using a feature set formed by Principal Component Analysis (PCA), Frequency Domain and Discrete Cosine Transform (DCT) components. The idea is to use these varied components to form a unique hybrid feature set so as to capture relevant details of objects for recognition using a ANN which for the work is a Multi Layer Perceptron (MLP) trained with (error) Back Propagation learning.
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© 2012 Springer-Verlag Berlin Heidelberg
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Barthakur, M., Thakuria, T., Sarma, K.K. (2012). Artificial Neural Network (ANN) Based Object Recognition Using Multiple Feature Sets. In: Patnaik, S., Yang, YM. (eds) Soft Computing Techniques in Vision Science. Studies in Computational Intelligence, vol 395. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25507-6_11
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DOI: https://doi.org/10.1007/978-3-642-25507-6_11
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