Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Construction of 2D Shape Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Tooth Contour Extraction for Matching Dental Radiographs
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
IEEE Transactions on Image Processing
Speckle reducing anisotropic diffusion
IEEE Transactions on Image Processing
webADIS: a flexible web-based environment for the Automated Dental Identification System
dg.o '06 Proceedings of the 2006 international conference on Digital government research
Automated dental identification system (ADIS)
dg.o '06 Proceedings of the 2006 international conference on Digital government research
Hierarchical contour matching for dental X-ray radiographs
Pattern Recognition
A Dental Radiograph Recognition System Using Phase-Only Correlation for Human Identification
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Forensic bite mark identification using image processing methods
Proceedings of the 2009 ACM symposium on Applied Computing
Enhanced human identification system using dental biometrics
NN'09 Proceedings of the 10th WSEAS international conference on Neural networks
Biometrics beyond the visible spectrum: imaging technologies and applications
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
Retrieving dental radiographs for post-mortem identification
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
3D dental biometrics: Alignment and matching of dental casts for human identification
Computers in Industry
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Dental biometrics utilizes dental radiographs for human identification. The dental radiographs provide information about teeth, including tooth contours, relative positions of neighboring teeth, and shapes of the dental work (e.g., crowns, fillings, and bridges). The proposed system has two main stages: feature extraction and matching. The feature extraction stage uses anisotropic diffusion to enhance the images and a Mixture of Gaussians model to segment the dental work. The matching stage has three sequential steps: tooth-level matching, computation of image distances, and subject identification. In the tooth-level matching step, tooth contours are matched using a shape registration method, and the dental work is matched on overlapping areas. The distance between the tooth contours and the distance between the dental work are then combined using posterior probabilities. In the second step, the tooth correspondences between the given query (postmortem) radiograph and the database (antemortem) radiograph are established. A distance based on the corresponding teeth is then used to measure the similarity between the two radiographs. Finally, all the distances between the given postmortem radiographs and the antemortem radiographs that provide candidate identities are combined to establish the identity of the subject associated with the postmortem radiographs.