Abstract
This paper analyzes the application of the High Order Artificial Neural Networks (HOANN) to recognition of patterns invariant to projectivity transformation. This invariance is necessary in those applications in which either the objects or the own vision system can be displaced or turned in the environment. The invariant relation incorporated to the structure of our neural system is the cross-ratio of sines of angles of five coplanar points [Barrett, 1991]. This relation limits the application of these networks to the recognition of flat objects or threedimensional objects which are characterized by some plane of symmetry. As application we introduce a high order neural architecture applied to the projectivity invariant discrimination of projective transformed patterns of different modern aircraft.
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References
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© 1997 Springer-Verlag Berlin Heidelberg
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Joya, G., Sandoval, F. (1997). Neural networks based projectivity invariant recognition of flat patterns. In: Mira, J., Moreno-Díaz, R., Cabestany, J. (eds) Biological and Artificial Computation: From Neuroscience to Technology. IWANN 1997. Lecture Notes in Computer Science, vol 1240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0032563
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DOI: https://doi.org/10.1007/BFb0032563
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