Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Communications of the ACM
Dental Biometrics: Alignment and Matching of Dental Radiographs
IEEE Transactions on Pattern Analysis and Machine Intelligence
A prototype automated dental identification system (ADIS)
dg.o '03 Proceedings of the 2003 annual national conference on Digital government research
Classification and numbering of teeth in dental bitewing images
Pattern Recognition
A system for human identification from X-ray dental radiographs
Pattern Recognition
Dental biometrics: human identification using dental radiographs
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Optimum design of chamfer distance transforms
IEEE Transactions on Image Processing
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
Boosting chamfer matching by learning chamfer distance normalization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Dental x-ray image segmentation and object detection based on phase congruency
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
3D dental biometrics: Alignment and matching of dental casts for human identification
Computers in Industry
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The goal of forensic dentistry is to identify individuals based on their dental characteristics. In this paper we present a new algorithm for human identification from dental X-ray images. The algorithm is based on matching teeth contours using hierarchical chamfer distance. The algorithm applies a hierarchical contour matching algorithm using multi-resolution representation of the teeth. Given a dental record, usually a postmortem (PM) radiograph, first, the radiograph is segmented and a multi-resolution representation is created for each PM tooth. Each tooth is matched with the archived antemortem (AM) teeth, which have the same tooth number, in the database using the hierarchical algorithm starting from the lowest resolution level. At each resolution level, the AM teeth are arranged in an ascending order according to a matching distance and 50% of the AM teeth with the largest distances are discarded and the remaining AM teeth are marked as possible candidates and the matching process proceeds to the following (higher) resolution level. After matching all the teeth in the PM image, voting is used to obtain a list of best matches for the PM query image based upon the matching results of the individual teeth. Analysis of the time complexity of the proposed algorithm prove that the hierarchical matching significantly reduces the search space and consequently the retrieval time is reduced. The experimental results on a database of 187 AM images show that the algorithm is robust for identifying individuals based on their dental radiographs.