In this paper, we present a parametric hybrid model used in the framework of multidimensional object representation, for applications to both object visualization and object-based data compression. Our model is defined as a set of hybrid ellipsoids suitable for both globally and locally deforming the reconstructed shape. Its new parameterization, as compared to classical techniques, allows us to preserve its analytical representation during the fitting process. It is fitted to the object contours by means of a genetic algorithm minimizing a mean-square error criterion. Several criteria are proposed and discussed according to the stability of the optimization process, as well as the ability to efficiently initialize the model parameters. Finally, fitting results are presented for 2D and 3D data and different applications are proposed
A parametric hybrid model used for multidimensional object representation
MENEGAZ, Gloria;
1999-01-01
Abstract
In this paper, we present a parametric hybrid model used in the framework of multidimensional object representation, for applications to both object visualization and object-based data compression. Our model is defined as a set of hybrid ellipsoids suitable for both globally and locally deforming the reconstructed shape. Its new parameterization, as compared to classical techniques, allows us to preserve its analytical representation during the fitting process. It is fitted to the object contours by means of a genetic algorithm minimizing a mean-square error criterion. Several criteria are proposed and discussed according to the stability of the optimization process, as well as the ability to efficiently initialize the model parameters. Finally, fitting results are presented for 2D and 3D data and different applications are proposed
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