Robust Moment Invariant with Higher Discriminant Factor Based on Fisher Discriminant Analysis for Symbol Recognition

Autor:	Widya Andyardja Weliamto, Antonius Wibowo, Hock Soon Seah
Rok vydání:	2006
Předmět:	Euclidean distance Normalization (statistics) Mahalanobis distance Discriminant business.industry Optimal discriminant analysis Pattern recognition Artificial intelligence Kernel Fisher discriminant analysis Invariant (mathematics) business Linear discriminant analysis Mathematics
Zdroj:	Lecture Notes in Computer Science ISBN: 9783540347118 GREC
DOI:	10.1007/11767978_37
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9a4aa47eea52787ebc99a58ff2785ebf https://doi.org/10.1007/11767978_37 Zobrazit plný text záznamu