A new method for image segmentation
Computer Vision, Graphics, and Image Processing
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching Widely Separated Views Based on Affine Invariant Regions
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Sparse Texture Representation Using Local Affine Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification of coins using an eigenspace approach
Pattern Recognition Letters
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Features for Object Class Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Simultaneous Object Recognition and Segmentation from Single or Multiple Model Views
International Journal of Computer Vision
Object Level Grouping for Video Shots
International Journal of Computer Vision
An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
2D shape classification and retrieval
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Detecting symmetry and symmetric constellations of features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Automatic extraction and classification of footwear patterns
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
On ancient coin classification
VAST'07 Proceedings of the 8th International conference on Virtual Reality, Archaeology and Intelligent Cultural Heritage
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Reliable object identification is an essential task in the process of recognition and traceability of stolen cultural heritage. Existing approaches for object recognition focus mainly on object classification. However, they are not sufficient to identify a given object among hundreds of objects of the same class. In this paper, we investigate the feasibility of computer aided identification of ancient coins. Since the shape of a coin is a very unique feature, we first apply a shape descriptor to capture its characteristics. In the next step, local features are used to describe the die information. We present experiments on a data set of 2400 images of ancient coins. The evaluation results show that our approach is competitive. Moreover, it indicates some outstanding features that show great promise for reliable object identification in the area of cultural heritage.