A survey of image registration techniques
ACM Computing Surveys (CSUR)
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Tooth Contour Extraction for Matching Dental Radiographs
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Parallel Algorithms for the Training Process of a Neural Network-Based System
International Journal of High Performance Computing Applications
Neural networks for fingerprint recognition
Neural Computation
Classification and numbering of teeth in dental bitewing images
Pattern Recognition
A system for human identification from X-ray dental radiographs
Pattern Recognition
Properties of cross-entropy minimization
IEEE Transactions on Information Theory
Automated dental identification system (ADIS)
dg.o '07 Proceedings of the 8th annual international conference on Digital government research: bridging disciplines & domains
Automated dental identification system (ADIS) in testing mode
dg.o '07 Proceedings of the 8th annual international conference on Digital government research: bridging disciplines & domains
Teeth/Palate and interdental segmentation using artificial neural networks
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
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This paper addresses the problem of creating a postmortem identification system by matching image features extracted from dental radiographs. We lay the architecture of a prototype automated dental identification system (ADIS), which tackles the dental image matching problem by first extracting high-level features to expedite retrieval of potential matches and then by low-level image comparison using inherent features of dental images. We propose the use of learnable inherent dental image features for tooth-to-tooth image comparisons. We treat the tooth-to-tooth matching problem as a binary classification problem for which we propose probabilistic models of class-conditional densities. We also propose an adaptive strategic searching technique and use it in conjunction with back propagation in order to estimate system parameters. We present promising experimental results that reflect the value of our approach.