Robust moment invariant with higher discriminant factor based on fisher discriminant analysis for symbol recognition

Authors:
Widya Andyardja Weliamto;Hock Soon Seah;Antonius Wibowo
Affiliations:
School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore;Department of Electrical Engineering, Bandung Institute of Technology, Indonesia
Venue:
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
Year:
2005

Citing 7
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GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
Robust moment invariant with higher discriminant factor based on fisher discriminant analysis for symbol recognition

GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives

Robust moment invariant with higher discriminant factor based on fisher discriminant analysis for symbol recognition

GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives

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Abstract

In this paper, we propose a robust moment invariant which has a higher discriminant factor based on Fisher linear discriminant analysis that can deal with noise degradation, deformation of vector distortion, translation, rotation and scale invariant. The proposed system for the symbol recognition consists of 3 steps: 1) degradation model preprocessing step, 2) a different normalization for the second moment invariant and a measure for roundness and eccentricity for feature extraction step, 3) k-Nearest Neighbor with Mahalanobis distance compared to Euclidean distance and k-D tree for classifier. A comparison using multi-layer feed forward neural network classifier is given. An improvement of the discriminant factor around 4 times is achieved compared to that of the original normalized second moments using GREC 2005 dataset. Experimentally we tested our system with 3300 training images using k-NN classifier and on all 9450 images given in the dataset and achieved recognition rates higher than 86 % for all degradation models and 96 % for degradation models 1 to 4.