Abstract
Many researchers have explored the relationship between recurrent neural networks and finite state machines. Finite state machines constitute the best characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. Recently, the neurally-inspired algorithmic lateral inhibition (ALI) method and its application to the motion detection task have been introduced. The article shows how to implement the tasks directly related to ALI in motion detection by means of a formal model described as finite state machines. Automata modeling is the first step towards real-time implementation by FPGAs and programming of ”intelligent” camera processors.
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References
Fernández-Caballero, A., Mira, J., Fernández, M.A., López, M.T.: Segmentation from motion of non-rigid objects by neuronal lateral interaction. Pattern Recognition Letters 22(14), 1517–1524 (2001)
Fernández-Caballero, A., Mira, J., Delgado, A.E., Fernández, M.A.: Lateral interaction in accumulative computation: A model for motion detection. Neurocomputing 50C, 341–364 (2003)
Fernández-Caballero, A., Fernández, M.A., Mira, J., Delgado, A.E.: Spatio-temporal shape building from image sequences using lateral interaction in accumulative computation. Pattern Recognition 36(5), 1131–1142 (2003)
Fernández-Caballero, A., Mira, J., Fernández, M.A., Delgado, A.E.: On motion detection through a multi-layer neural network architecture. Neural Networks 16(2), 205–222 (2003)
Mira, J., Delgado, A.E., Fernández-Caballero, A., Fernández, M.A.: Knowledge modelling for the motion detection task: The lateral inhibition method. Expert Systems with Applications 7(2), 169–185 (2004)
López, M.T., Fernández-Caballero, A., Fernández, M.A., Mira, J., Delgado, A.E.: Visual surveillance by dynamic visual attention method. Pattern Recognition 39(11), 2194–2211 (2006)
López, M.T., Fernández-Caballero, A., Fernández, M.A., Mira, J., Delgado, A.E.: Motion features to enhance scene segmentation in active visual attention. Pattern Recognition Letters 27(5), 469–478 (2006)
Ñeco, R.P., Forcada, M.L.: Asynchronous translations with recurrent neural nets. In: Proceedings of the International Conference on Neural Networks, ICNN’97, vol. 4, pp. 2535–2540 (1997)
McCulloch, W.S., Pitts, W.H.: A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5, 115–133 (1943)
Kleene, S.C.: Representation of events in nerve nets and finite automata. In: Automata Studies, Princeton University Press, Princeton (1956)
Minsky, M.L.: Computation: Finite and Infinite Machines. Prentice-Hall, Englewood Cliffs (1967)
Carrasco, R.C., Oncina, J., Forcada, M.L.: Efficient encoding of finite automata in discrete-time recurrent neural networks. In: Proceedings of the Ninth International Conference on Artificial Neural Networks, ICANN’99, vol. 2, pp. 673–677 (1999)
Forcada, M.L., Carrasco, R.C.: Finite-state computation in analog neural networks: Steps towards biologically plausible models? In: Wermter, S., Austin, J., Willshaw, D. (eds.) Emergent Neural Computational Architectures Based on Neuroscience. LNCS (LNAI), vol. 2036, pp. 480–486. Springer, Heidelberg (2001)
Prat, F., Casacuberta, F., Castro, M.J.: Machine translation with grammar association: Combining neural networks and finite state models. In: Proceedings of the Second Workshop on Natural Language Processing and Neural Networks, pp. 53–60 (2001)
Sun, G.Z., Giles, C.L., Chen, H.H.: The Neural Network Pushdown Automaton: Architecture, Dynamics and Training. In: Giles, C.L., Gori, M. (eds.) IIASS-EMFCSC-School 1997. LNCS (LNAI), vol. 1387, pp. 296–345. Springer, Heidelberg (1998)
Cleeremans, A., Servan-Schreiber, D., McClelland, J.L.: Finite state automata and simple recurrent networks. Neural Computation 1(3), 372–381 (1989)
Giles, C.L., Miller, C.B., Chen, D., Chen, H.H., Sun, G.Z., Lee, Y.C.: Learning and extracted finite state automata with second-order recurrent neural networks. Neural Computation 4(3), 393–405 (1992)
Manolios, P., Fanelli, R.: First order recurrent neural networks and deterministic finite state automata. Neural Computation 6(6), 1154–1172 (1994)
Gori, M., Maggini, M., Martinelli, E., Soda, G.: Inductive inference from noisy examples using the hybrid finite state filter. IEEE Transactions on Neural Networks 9(3), 571–575 (1998)
Bensaali, F., Amira, A.: Accelerating colour space conversion on reconfigurable hardware. Image and Vision Computing 23, 935–942 (2005)
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Mira, J., Delgado, A.E., Fernández-Caballero, A., López, M.T., Fernández, M.A. (2007). The Underlying Formal Model of Algorithmic Lateral Inhibition in Motion Detection. In: Mira, J., Álvarez, J.R. (eds) Nature Inspired Problem-Solving Methods in Knowledge Engineering. IWINAC 2007. Lecture Notes in Computer Science, vol 4528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73055-2_14
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DOI: https://doi.org/10.1007/978-3-540-73055-2_14
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