Three-dimensional object recognition
ACM Computing Surveys (CSUR) - Annals of discrete mathematics, 24
Segmentation through Variable-Order Surface Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evidence-Based Recognition of 3-D Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Model-based object recognition in dense-range images—a review
ACM Computing Surveys (CSUR)
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Simplified Gaussian and Mean Curvatures to Range Image Segmentation
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Visual routines for eye location using learning and evolution
IEEE Transactions on Evolutionary Computation
Local binary patterns variants as texture descriptors for medical image analysis
Artificial Intelligence in Medicine
Combining different local binary pattern variants to boost performance
Expert Systems with Applications: An International Journal
International Journal of Multimedia Data Engineering & Management
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This paper describes a new medical image analysis technique for polygon mesh surfaces of human faces for a medical diagnosis application. The goal is to explore the natural patterns and 3D facial features to provide diagnostic information for Fetal Alcohol Syndrome (FAS). Our approach is based on a digital geometry analysis framework that applies pattern recognition techniques to digital geometry (polygon mesh) data from 3D laser scanners and other sources. Novel 3D geometric features are extracted and analyzed to determine the most discriminatory features that best represent FAS characteristics. As part of the NIH Consortium for FASD, the techniques developed here are being applied and tested on real patient datasets collected by the NIH Consortium both within and outside the US.