The Generalized Gabor Scheme of Image Representation in Biological and Machine Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning - Special issue on learning with probabilistic representations
Learning in graphical models
A Handwritten Numeral Character Classification Using Tolerant Rough Set
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Extraction of Hybrid Complex Wavelet Features for the Verification of Handwritten Numerals
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
A wavelet based multiresolution algorithm for rotation invariant feature extraction
Pattern Recognition Letters
Comparison and fusion of multiresolution features for texture classification
Pattern Recognition Letters
Gabor wavelet similarity maps for optimising hierarchical road sign classifiers
Pattern Recognition Letters
Gabor features for offline Arabic handwriting recognition
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
An automated system for the grading of diabetic maculopathy in fundus images
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Detection and classification of retinal lesions for grading of diabetic retinopathy
Computers in Biology and Medicine
Automated detection of exudates and macula for grading of diabetic macular edema
Computer Methods and Programs in Biomedicine
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We present a method of handwritten numeral recognition, where we introduce hierarchical Gabor features (HGFs) and construct a Bayesian network classifier that encodes the dependence between HGFs. We extract HGFs in such a way that they represent different levels of information which are structured such that the lower the level is, the more localized information they have. At each level, we choose an optimal set of 2-D Gabor filters in the sense that Fisher's linear discriminant (FLD) measure is maximized and these Gabor filters are used to extract HGFs. We construct a Bayesian network classifier that encodes hierarchical dependence among HGFs. We confirm the useful behavior of our proposed method, comparing it with the naive Bayesian classifier, k-nearest neighbor, and an artificial neural network, in the task of handwritten numeral recognition.