• Journal of computational fluids engineering
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• v.2 no.2
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• pp.97-103
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• 1997

A modified Delaunay triangulation technique is tested for complicated computational domain. While a simple geometry. both in topology and geometry, has been well discretized into triangular elements, a complex geometry having difficulty in triangulation had to be divided into small sub-domains of simpler shape. The present study presents a modified Delaunay triangulation method based on the data structure of geometric modeller. This approach greatly enhances the reliability of triangulation, especially in complicated computational domain. We have shown that efficiency of Delaunay triangulation can be much improved by using both the GUI (Graphic User Interface) and OOP (Object-Oriented Programming).

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.10
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• pp.520-529
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• 2005

A feature-based 3D modeling algorithm is presented in this paper. Since conventional methods use depth-based techniques, they need much time for the image matching to extract depth information. Even feature-based methods have less computation load than that of depth-based ones, the calculation of modeling error about whole pixels within a triangle is needed in feature-based algorithms. It also increase the computation time. Therefore, the proposed algorithm consists of three phases, which are an initial 3D model generation, model evaluation, and model refinement phases, in order to acquire an efficient 3D model. Intensity gradients and incremental Delaunay triangulation are used in the Initial model generation. In this phase, a morphological edge operator is adopted for a fast edge filtering, and the incremental Delaunay triangulation is modified to decrease the computation time by avoiding the calculation errors of whole pixels and selecting a vertex at the near of the centroid within the previous triangle. After the model generation, sparse vertices are matched, then the faces are evaluated with the size, approximation error, and disparity fluctuation of the face in evaluation stage. Thereafter, the faces which have a large error are selectively refined into smaller faces. Experimental results showed that the proposed algorithm could acquire an adaptive model with less modeling errors for both smooth and abrupt areas and could remarkably reduce the model acquisition time.