Convolutional neural networks (CNNs) represent an interesting method for adaptive image processing, and form a link between general feed-forward neural networks and adaptive filters. Two-dimensional CNNs are formed by one or more layers of two-dimensional filters, with possible non-linear activation functions and/or down-sampling. Convolutional neural networks (CNNs) impose constraints on the weights and connectivity of the network, providing a framework well suited to the processing of spatially or temporally distributed data. CNNs possess key properties of translation invariance and spatially local connections (receptive fields). The socalled “weight-sharing” property of CNNs limits the number of free parameters. Although CNNs have been applied to face and character recognition, it is fair to say that the full potential of CNNs has not yet been realised. This chapter presents a description of the convolutional neural network architecture, and reports some of our work applying CNNs to theoretical and real-world image processing problems.
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Browne, M., Ghidary, S.S., Mayer, N.M. (2008). Convolutional Neural Networks for Image Processing with Applications in Mobile Robotics. In: Prasad, B., Prasanna, S.R.M. (eds) Speech, Audio, Image and Biomedical Signal Processing using Neural Networks. Studies in Computational Intelligence, vol 83. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75398-8_15
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