Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31743
Integrating Low and High Level Object Recognition Steps by Probabilistic Networks

Authors: András Barta, István Vajk


In pattern recognition applications the low level segmentation and the high level object recognition are generally considered as two separate steps. The paper presents a method that bridges the gap between the low and the high level object recognition. It is based on a Bayesian network representation and network propagation algorithm. At the low level it uses hierarchical structure of quadratic spline wavelet image bases. The method is demonstrated for a simple circuit diagram component identification problem.

Keywords: Object recognition, Bayesian network, Wavelets, Document processing.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1399


[1] Barta A, Vajk I., Document Image Analysis by Probabilistic Network and Circuit Diagram Extraction, Informatica, An International Journal of Computing and Informatics, 29, pp. 291-301, 2005
[2] Barta A., Vajk I, Processing Circuit Diagrams with Belief Network and Intelligent Agents., Transactions on Information Science and Applications, Issue 9, Vol. 2, September, pp. 1321-1329, 2005
[3] Draper B., Hanson H., Riseman E., Knowledge-Directed Vision: Control, Learning and Integration, ~draper/ publications/draper_ieee96.pdf Proceedings of the IEEE, 84(11), pp. 1625-1637, 1996
[4] Neopolitan R. E., Learning Bayesian networks, Pearson Prentice Hall, 2004
[5] Pearl, J., Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Interference, Morgan Kauffmann Publishers, 1988
[6] Okazaki A., Kondo T., Mori K., Tsunekawa S., Kawamoto E., An Automatic Circuit Diagram Reader With Loop-Structure-Based Symbol Recognition, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 10, No. 3, pp. 331-341, May 1988
[7] Takatsuka M., Caelli T. M., West G. A. W., Venkatesh S., An application of ÔÇÿÔÇÿagent-oriented-- techniques to symbolic matching and object recognition, Pattern Recognition Letters 23, pp. 419-429, 2002
[8] Siddiqi K., Subrahmonia J., Cooper D., Kimia B.B., Part-Based Bayesian Recognition Using Implicit Polynomial Invariants, Proceedings of the 1995 International Conference on Image Processing (ICIP), pp. 360-363, 1995
[9] Storkey A.J., Williams C.K.I., Image Modeling with Position-Encoding Dynamic Trees, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 25, No. 7, July pp. 859-871, 2003
[10] Zou Song-Chun, Statistical Modeling and Conceptualization of Visual Patterns, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 25, No. 6, June, pp 691-712, 2003
[11] Mallat S., A Wavelet Tour of Signal Processing, Academic Press, 1999
[12] Burrus C. S, Introduction to Wavelets and Wavelet Transforms, Prentice Hall, 1998
[13] Freeman T.W., Adelson E.H., The Design and Use of Steerable Filters, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 13, No. 9, September, pp 891-906, 1991
[14] Sung J., Bang S.J., Choi S., A Bayesian network classifier and hierarchical Gabor features for Handwritten Numeral Recognition, Pattern Recognition Letters, 27. pp 66-75, 2006
[15] Deng S., Lati S., Regentova E., Document segmentation using polynomial spline wavelets, Pattern Recognition, 34. pp. 2533-2545, 2001
[16] Olshausen B.A, Anderson C.H., Van Essen D.C., A neurobiological model of visual attention and invariant pattern recognition based dynamic routing information, The Journal of Neuroscience, 13(11), 4700-4719, 1993