A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The complexity of the matrix eigenproblem
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
SVM binary classifier ensembles for image classification
Proceedings of the tenth international conference on Information and knowledge management
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume II
ISCV '95 Proceedings of the International Symposium on Computer Vision
Journal of Cognitive Neuroscience
A Fast Correlation Method for Scale-and Translation-Invariant Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Translation, rotation, and scale-invariant object recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Image Processing
Rotation-invariant neural pattern recognition system with application to coin recognition
IEEE Transactions on Neural Networks
An assembled matrix distance metric for 2DPCA-based image recognition
Pattern Recognition Letters
Recognizing Ancient Coins Based on Local Features
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Numismatic Object Identification Using Fusion of Shape and Local Descriptors
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Design of Searchable Commemorative Coins Image Library
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Image based recognition of ancient coins
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
A fast and reliable coin recognition system
Proceedings of the 29th DAGM conference on Pattern recognition
From manual to automated optical recognition of ancient coins
VSMM'07 Proceedings of the 13th international conference on Virtual systems and multimedia
Gabor wavelet based automatic coin classsification
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Front end analysis of speech recognition: a review
International Journal of Speech Technology
Content-Based coin retrieval using invariant features and self-organizing maps
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
On ancient coin classification
VAST'07 Proceedings of the 8th International conference on Virtual Reality, Archaeology and Intelligent Cultural Heritage
Automatic coin classification by image matching
VAST'11 Proceedings of the 12th International conference on Virtual Reality, Archaeology and Cultural Heritage
Reading ancient coins: automatically identifying denarii using obverse legend seeded retrieval
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Coarse-to-Fine correspondence search for classifying ancient coins
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
International Journal of Speech Technology
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We present a vision-based approach to coin classification which is able to discriminate between hundreds of different coin classes. The approach described is a multistage procedure. In the first stage a translationally and rotationally invariant description is computed. In a second stage an illumination-invariant eigenspace is selected and probabilities for coin classes are derived for the obverse and reverse sides of each coin. In the final stage coin class probabilities for both coin sides are combined through Bayesian fusion including a rejection mechanism. Correct decision into one of the 932 different coin classes and the rejection class, i.e., correct classification or rejection, was achieved for 93.23% of coins in a test sample containing 11,949 coins. False decisions, i.e., either false classification, false rejection or false acceptance, were obtained for 6.77% of the test coins.