Abstract
This article describes an approach to automatically recognize patterns such as 3D objects and handwritten digits based on a database. The designed system can be used for both 3D object recognition from 2D poses of the object and handwritten digit recognition applications. The system does not require any feature extraction stage before the recognition. Probabilistic Neural Network (PNN) is used for the recognition of the patterns. Experimental results show that pattern recognition by the proposed method improves the recognition rate considerably. The system has been compared to other network structures in terms of speed and accuracy and has shown better performance in simulations.
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References
Li, B.Q., Li, B.X.: Building Pattern Classifiers Using Convolutional Neural Networks. IJCNN’ 99. International Joint Conference on Neural Networks. 5 (1999) 3081–3085
Goh, T. C.: Probabilistic Neural Network for Evaluating Seismic Liquefaction Potential. Can. Geotech. J., 39 (2002) 219–232
Egmont-Petersen, M., de Ridder, D., Handels, H.: Image Processing with Neural Networks — A Review. The Journal of the Pattern Recognition Society, 35(10) (2002) 2279–2301
Zhao, L.W., Luo, S.W., Liao, L.Z.: 3D Object Recognition and Pose Estimation Using Kernel PCA. Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 5 (2004) 3258–3262
Okamoto, J., Jr., Milanova, M., Bueker, U.: Active Perception System for Recognition of 3D Objects in Image Sequences. 5th International Workshop on Advanced Motion Control. AMC’ 98-Coimbra. 29 (1998) 700–705
Polat, O., Tavsanoglu, V.: 3-D Object Recognition Using 2-D Poses Processed by CNNs and a GRNN. Proc. of 2005 Turkish Symposium Artificial Intelligence and Neural Networks. Cesme, Turkey (June 2005)
Nebauer, C.: Evaluation of Convolutional Neural Networks for Visual Recognition. IEEE Transactions on Neural Networks, Vol. 9, Issue 4. (July 1998) 685–696
Fasel, B.: Head-pose Invariant Facial Expression Recognition Using Convolutional Neural Networks. Proc. of 2002 Fourth IEEE International Conference on Multimodal Interfaces. (2002) 529–534
Simard, P.Y., Steinkraus, D., Platt, J.C.: Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis. Proc. of the 2003 Seventh International Conference on Document Analysis and Recognition, (2003) 958–963
Comes, B., Kelemen, A.: Probabilistic Neural Network Classification for Microarraydata. Proceedings of the International Joint Conference on Neural Networks, 3 (2003) 1714–1717
Parzen, E.: On Estimation of A Probability Density Function and Model, Annals of Mathematical Statistics, 36 (1962) 1065–1076
Sameer, A. N., Shree, K. N., Hiroshi M.: Columbia Object Image Library (COIL-100). Technical Report No. CUCS-006-96, Department of Computer Science Columbia University New York, N.Y. 10027
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© 2006 Springer-Verlag Berlin Heidelberg
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Polat, Ö., Yıldırım, T. (2006). Pattern Recognition Without Feature Extraction Using Probabilistic Neural Network. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing in Signal Processing and Pattern Recognition. Lecture Notes in Control and Information Sciences, vol 345. Springer, Berlin, Heidelberg . https://doi.org/10.1007/978-3-540-37258-5_41
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DOI: https://doi.org/10.1007/978-3-540-37258-5_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37257-8
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