A unifying framework for invariant pattern recognition

Pattern Recognition Letters

Volumn 17, Issue 14, 1996, Pages 1415-1422

  Wood, Jeffrey ^a   Shawe Taylor, John ^a  

^a UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Discrete Fourier transform; Fast translation invariant transform; Group theory; Higher order networks; Invariant neural networks; Invariant pattern recognition

Indexed keywords

FOURIER TRANSFORMS; INVARIANCE; MATHEMATICAL MODELS; NEURAL NETWORKS;

FAST TRANSLATION INVARIANT TRANSFORM; GROUP THEORY; HIGHER ORDER NETWORKS;

PATTERN RECOGNITION;

EID: 0042221577     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-8655(96)00103-1     Document Type: Article

References (12)

1. Caelli, T.M. and Zhi-Qiang Liu (1988). On the minimum number of templates required for shift, rotation and size invariant pattern recognition. Pattern Recognition 21 (3), 205-216.
2. Fukumi, M., S. Omatu, F. Takeda and T. Kosaka (1992). Rotation-invariant neural pattern recognition system with application to coin recognition. IEEE Trans. on Neural Networks 3 (2), 272-279.
3. Fukushima, K. and S. Miyake (1982). Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position. Pattern Recognition 15 (6), 445-469.
4. Giles, C.L. and T. Maxwell (1987). Learning, invariance, and generalization in high-order neural networks. Appl. Opt. 26 (23), 4972-8.
5. Le Cun, Y., B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard and L. Jackel (1989). Backpropagation applied to handwritten zip code recognition. Neural Computation 1, 541-551.
6. Li, Y. (1992). Reforming the theory of invariant moments for pattern recognition. Pattern Recognition 25 (7), 723-730.
7. Rumelhart, D.E., G.E. Hinton and R.J. Williams (1986) Learning internal representations by error propagation. In: Parallel Distributed Processing: Explorations in the Microstructures of Cognition 1. MIT Press, Cambridge, MA, 318-362.
8. Shawe-Taylor, J. (1989). Building symmetries into feed-forward networks. In: Proc. First IEE Conf. on Artificial Neural Networks, 158-162.
9. Wagh, M.D. and S.V. Kanetkar (1977). A class of translation invariant transforms. IEEE Trans. Acoust. Speech Signal Process. 25, 203.
10. Waibel, A., T. Hanazawa, G. Hinton, K. Shikano and K. Lang (1989). Phoneme recognition using time-delay neural networks. IEEE Trans. Acoust. Speech Signal Process. 37 (3), 328-339.
11. Wood, J. (1995). A model of adaptive invariance. PhD thesis, University of London.
12. Wood, J. and J. Shawe-Taylor (1996). Representation theory and invariant neural networks. Discrete Appl. Math. 69, 33-60.