Abstract
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. CNNs possess key properties of translation invariance and spatially local connections (receptive fields). We present a description of the convolutional network architecture, and an application to practical image processing on a mobile robot. A CNN is used to detect and characterize cracks on an autonomous sewer inspection robot. The filter sizes used in all cases were 4x4, with non-linear activations between each layer. The number of feature maps used in the three hidden layers was, from input to output, 4, 4, 4. The network was trained using a dataset of 48x48 sub-regions drawn from 30 still image 320x240 pixel frames sampled from a pre-recorded sewer pipe inspection video. 15 frames were used for training and 15 for validation of network performance. Although development of a CNN system for civil use is on-going, the results support the notion that data-based adaptive image processing methods such as CNNs are useful for image processing, or other applications where the input arrays are large, and spatially / temporally distributed. Further refinements of the CNN architecture, such as the implementation of separable filters, or extensions to three dimensional (ie. video) processing, are suggested.
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Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representation by error propagation. In: Rumelhart, D.E., McClelland, J.L. (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, pp. 318–362. MIT Press, Cambridge (1986)
Le Cun, Y.B., Boser, J.S., Denker, D., Henderson, R.E., Howard, W., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Computation 4(1), 541–551 (1988)
Lang, K.J., Hinton, G.E.: Dimensionality reduction and prior knowledge in e-set recognition. In: Touretzky, D.S. (ed.) Advances in Neural Information Processing Systems, pp. 178–185. Morgan Kauffman, San Marteo (1990)
Fukushima, K., Miyake, S., Ito, T.: Neocognitron: a neural model for a mechanism of visual pattern recognition. IEEE Transactions on Systems, Man, and Cybernetics 13, 826–834 (1983)
Fukushima, K.: Neocognitron: A hierachical neural network capable of visual pattern recognition. Neural Networks 1(2), 119–130 (1988)
Le Cun, Y., Bengio, Y.: Convolutional networks for images, speech, and time series. In: Arbib, M.A. (ed.) The Handbook of Brain Theory and Neural Networks, pp. 255–258. MIT Press, Cambridge (1995)
Lawrence, S., Lee Giles, C., Tsoi, A.C., Back, A.D.: Face recognition: A convolutional neural network approach. IEEE Transactions on Neural Networks 8(1), 98–113 (1997)
Fasel, B.: Robust face analysis using convolutional neural networks. In: Proceedings of the International Conference on Pattern Recognition (ICPR 2002), Quebec, Canada (2002)
Sackinger, E., Boser, B., Bromley, J., LeCun, Y.: Application of the anna neural network chip to high-speed character recognition. IEEE Transactions on Neural Networks 3, 498–505 (1992)
Le Cun, Y.: Generalization and network design strategies,” Tech. Rep. CRGTR- 89-4, Department of Computer Science, University of Toronto (1989)
Bengio, Y., Le Cun, Y., Henderson, D.: Globally trained handwritten word recognizer using spatial representation, convolutional neural networks, and Hidden MarkovModels. In: Cowan, J.D., Tesauro, G., Alspector, J. (eds.) Advances in Neural Information Processing Systems, vol. 6, pp. 937–944. Morgan Kaufmann Publishers, Inc., San Francisco (1994)
Fasel, B.: Facial expression analysis using shape and motion information extracted by convolutional neural networks. In: Proceedings of the International IEEE Workshop on Neural Networks for Signal Processing (NNSP 2002), Martigny, Switzerland (2002)
Kirchner, F., Hertzberg, J.: A prototype study of an autonomous robot platform for sewerage system maintenance. Autonomous Robots 4(4), 319–331 (1997)
Browne, M., Dorn, M., Ouellette, R., Shiry, S.: Wavelet entropy-based feature extraction for crack detection in sewer pipes. In: 6th International Conference on Mechatronics Technology, Kitakyushu, Japan (2002)
Browne, M., Shiry, S., Dorn, M., Ouellette, R.: Visual feature extraction via pca-based parameterization of wavelet density functions. In: International Symposium on Robots and Automation, Toluca, Mexico (2002)
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© 2003 Springer-Verlag Berlin Heidelberg
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Browne, M., Ghidary, S.S. (2003). Convolutional Neural Networks for Image Processing: An Application in Robot Vision. In: Gedeon, T.(.D., Fung, L.C.C. (eds) AI 2003: Advances in Artificial Intelligence. AI 2003. Lecture Notes in Computer Science(), vol 2903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24581-0_55
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DOI: https://doi.org/10.1007/978-3-540-24581-0_55
Publisher Name: Springer, Berlin, Heidelberg
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