Model-based recognition and tracking from 2-D images depends upon the ability to solve for projection and model parameters that will best fit a 3-D model to matching image features. This paper extends current methods of parameter solving to handle objects with arbitrary curved surfaces and with any number of internal parameters representing articulations, variable dimensions, or surface deformations. Numerical stabilization methods are developed that take account of inherent inaccuracies in the image measurements and allow useful solutions to be determined even when there are fewer matches than unknown parameters. A standardized modeling language has been developed that can be used to define models and their internal parameters for efficient application to model-based vision. These new techniques allow model- based vision to be used for a much wider class of problems than was possible with earlier methods.
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