Abstract
A novel approach to create a general vision system is presented. The proposed method is based on a visual grammar representation which is transformed to a Bayesian network which is used for object recognition. We use a symbol-relational grammar for a hierarchical description of objects, incorporating spatial relations. The structure of a Bayesian network is obtained automatically from the grammar, and its parameters are learned from examples. The method is illustrated with two examples for face recognition.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Yu, Q., Cheng, H.H., Cheng, W.W., Zhou, X.: Ch opencv for interactive open architecture computer vision (2004)
Ferrucci, F., Pacini, G., Satta, G., Sessa, M.I., Tortora, G., Tucci, M., Vitiello, G.: Symbol-relation grammars: a formalism for graphical languages. Inf. Comput. 131(1), 1–46 (1996)
Fukushima, K.: Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36(4), 193–202 (1980)
Gabor, D.: Theory of communication. JIEE 93(3), 429–459 (1946)
Melendez, A., Sucar, L., Morales, E.: A visual grammar for face detection. In: Kuri-Morales, A., Simari, G. (eds.) IBERAMIA 2010. LNCS, vol. 6433, pp. 493–502. Springer, Heidelberg (2010) 10.1007/978-3-642-16952-6-50
Neapolitan, R.E.: Learning Bayesian Networks. Prentice Hall, Englewood Cliffs (2003)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo (1988)
Poggio, T., Serre, T., Kouh, M., Cadieu, C., Knoblich, U., Kreiman, G.: A theory of object recognition: computations and circuits in the feedforward path of the ventral stream in primate visual cortex. Technical Report CBCL-259, MIT Artificial Intelligence Laboratory (December 19, 2005)
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vision 57, 137–154 (2004)
Zhu, S.C., Mumford, D.: A stochastic grammar of images. Foundations and Trends in Computer Graphics and Vision 2(4), 259–362 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ruiz, E., Melendez, A., Sucar, L.E. (2011). Towards a General Vision System Based on Symbol-Relation Grammars and Bayesian Networks. In: Schmidhuber, J., Thórisson, K.R., Looks, M. (eds) Artificial General Intelligence. AGI 2011. Lecture Notes in Computer Science(), vol 6830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22887-2_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-22887-2_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22886-5
Online ISBN: 978-3-642-22887-2
eBook Packages: Computer ScienceComputer Science (R0)