3D analysis of tooth surfaces to aid accurate brace placement

Master'sMASTER OF ENGINEERIN

Three-dimensional object recognition

In the development of an object pattern recognition system, feature construction is always the problem issue. Due to the large amount of information contained in three dimensional (3D) objects, features extracted to efficiently and sufficiently represent 3D objects are difficult to obtain. Thus, current commercially available object recognition systems mostly emphasize the classification of two dimensional objects or patterns. This work presents a paradigm to develop a complete 3D object recognition system that uses simple and efficient features, and supports the integration of CAD/CAM models;In this research, several proposed algorithm for extracting features representing 3D objects are constructed based on the properties of the Radon transform. Two of these algorithms have been successfully implemented for manufacturing applications. The implemented systems use the artificial neural network as the classifier to learn features and to identify 3D objects. A statistical model has also been established based on the output node values of a perceptron neural network to predict the future misclassifications of features which have not been learned by the neural network in the training stage

Computational Strategies for Object Recognition

This article reviews the available methods forautomated identification of objects in digital images. The techniques are classified into groups according to the nature of the computational strategy used. Four classes are proposed: (1) the s~mplest strategies, which work on data appropriate for feature vector classification, (2) methods that match models to symbolic data structures for situations involving reliable data and complex models, (3) approaches that fit models to the photometry and are appropriate for noisy data and simple models, and (4) combinations of these strategies, which must be adopted in complex situations Representative examples of various methods are summarized, and the classes of strategies are evaluated with respect to their appropriateness for particular applications

MODEL-BASED RECOGNITION USING 3D SHAPE ALONE

The author shows that shape data alone, without absolute size, are highly effective in constraining the size of the search space of matches to stored 3D object models. The shape constraints developed are applied to sparse and error-prone measurements of surface orientations and scaled depths (that is, depths scaled by a constant but unknown factor) synthesized from polyhedral models which themselves have six degrees of positional freedom with respect to the sensor. The matching paradigm used is that of Grimson and Lozano-Perez in which feasible interpretations of the data are obtained by requiring geometric consistency between metrics made on pairs of data and their associated matched pair of model faces and then tested by geometrical transformation