Recognition of Patterns Without Feature Extraction by GRNN

  • Övünç Polat
  • Tülay Yıldırım
Conference paper

DOI: 10.1007/978-3-540-71629-7_19

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4432)
Cite this paper as:
Polat Ö., Yıldırım T. (2007) Recognition of Patterns Without Feature Extraction by GRNN. In: Beliczynski B., Dzielinski A., Iwanowski M., Ribeiro B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4432. Springer, Berlin, Heidelberg

Abstract

Automatic pattern recognition is a very important task in many applications such as image segmentation, object detection, etc. This work aims to find a new approach to automatically recognize patterns such as 3D objects and handwritten digits based on a database using General Regression Neural Networks (GRNN). 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 preprocessing and feature extraction stage before the recognition. Simulation results show that pattern recognition by GRNN improves the recognition rate considerably in comparison to other neural network structures and has shown better recognition rates and much faster training times than that of Radial Basis Function and Multilayer Perceptron networks for the same applications.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Övünç Polat
    • 1
  • Tülay Yıldırım
    • 1
  1. 1.Electronics and Communications Engineering Department, Yıldız Technical University, Besiktas, Istanbul 34349Turkey

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