Abstract
The ability to learn fundamental symbolic concepts, such as geometry and shape, is of interest both for complex industrial applications and for general research problems in intelligent machine vision. To these ends, the current Level I ALISA system has been enhanced to perform multi-class classification, and extended to Level II to learn and classify geometric concepts based on the texture class maps generated by Level I. Initial experiments demonstrate the successful classification and generalization of canonical and secular geometric concepts at Level II.
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P. Bock, R. Klinnert, R. Kober, R. M. Rovner, and H. Schmidt, “Gray-Scale ALIAS”, IEEE Transactions on Knowledge and Data Engineering, vol. 4, no. 2, April 1992.
P. Bock, The Emergence of Artificial Cognition: An Introduction to Collective Learning, World Scientific Publishing Company, New Jersey, 1993.
P. Bock, C.J. Kocinski, and R. Rovner, “A Performance Evaluation of ALIAS for the Detection of Geometric Anomalies on Fractal Images”, Advanced Neural Computers, pp. 237–246, Elsevier North-Holland, The Netherlands, July 1990.
R. Kober, C.G. Howard, and P. Bock, “The Detection of Anomalies in Video Images”, Proceedings of the International Workshop Neuro-Nimes '92: Neural Networks & Their Applications, November 2–6, 1992.
P. Bock, J. Hubshman, and M. Achikian, “Detection of Targets in Terrain Images with ALIAS”, Proceedings of the Twenty-Third Annual Pittsburgh Conference on Modeling and Simulation, pps 927–942, April 1992.
H. Niemann, Pattern Analysis and Understanding, 2nd Edition, Springer-Verlag Berlin Heidelberg, 1989.
R.O. Duda & P.E. Hart, Pattern Classification and Scene Analysis, John Wiley & Sons, New York, 1973.
Poggio, T., “Early vision: From computational structure to algorithms and parallel hardware”, in Rosenfeld, A., Ed., Human and Machine Vision II, Academic Press, Inc., Orlando, FL, 1986, pp. 190–206.
Marr, D., Vision: A Computational Investigation into Human Representation and Processing of Visual Information, Freeman, San Francisco, 1982.
Treisman, A., “Preattentive processing in vision”, in Rosenfeld, A., Ed., Human and Machine Vision II, Academic Press, Inc., Orlando, FL, 1986, pp. 313–334.
Pomerantz, J.R., “Are complex visual features derived from simple ones?”, in Leeuwenberg, E.L.J. and H.F.J.M. Buffart (Eds.), Formal Theories of Visual Perception, John Wiley & Sons, New York, 1978, Chapter 10, pp. 217–229.
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© 1993 Springer-Verlag Berlin Heidelberg
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Howard, C.G., Bock, P. (1993). Multi-class classification and symbolic cognitive processing with ALISA. In: Chetverikov, D., Kropatsch, W.G. (eds) Computer Analysis of Images and Patterns. CAIP 1993. Lecture Notes in Computer Science, vol 719. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57233-3_46
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DOI: https://doi.org/10.1007/3-540-57233-3_46
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