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An algorithm of effective feature line extraction for triangle mesh model is proposed. Feature line extraction of triangle mesh model is not only the key technology of revere engineering, but also an important research project of computer vision and machine intelligence. In the paper, firstly the principal curvatures and principal directions of vertex of triangle mesh model are analyzed and feature vertex is confirmed, then the method of “seed growth” to extract feature line is proposed. Experiments and applications show that the method is not only simple and extracting feature rapid, but also extract stably and exactly feature line.

In this paper, we proposed an improved version of the marching cube algorithm which gives a topologically correct triangular approximation of the isosurface for any cube configuration. First, a classification and characterization of critical points on the isosurface of trilinear functions is studied in detail. Then, unlike the past work on marching cube algorithm, a robust triangulation strategy without using the conventional look-up table and complementary and rotation operations is presented. Our algorithm is adaptive to the small changes of the data or the small changes of the threshold, and obtains more reasonable result of triangulation of isosurface than those produced by standard MC algorithm

The initialization is an important step in the level set method. However, it is a computational time-consuming step. In order to speed up the level set evolution procedures, we first introduce a new initialization algorithm based on the vector distance transform, which propagates a vector with a position of the nearest object pixel instead of the scalar distance. A new sign map labeling method, based on the flood fill, is proposed to distinguish the inside and outside of the 2D closed active contour. The active contour model proposed by Chan and Vese (Chan and Vese, 2001) can detect object whose boundaries are not necessarily defined by gradient. Our additional contribution to this paper is to present a further improvement to C-V model by replacing /spl delta/(/spl Phi/) with |/spl nabla//spl Phi/| to gain the global optimization. Finally, we illustrate the efficiency and performance of the proposed model by experimental results.

To estimate UAV(unmanned aerial vehicle) pose relative to airfield runway,a posterior parameter estimation method using the runway image and onboard pose sensors is presented.Firstly,pose parameters of UAV are measured by onboard sensors such as altimeter,GPS(global positioning system) and INS(inertial navigation system).Supposing the prior probability distribution of each parameter's measurement error is given and all errors are independent,the measured values from sensors can be considered as a sample of real ones.At the same time of sampling,an image of the runway is acquired from onboard calibrated camera,and the two edge lines of the runway are assumed to be extracted exactly from the image. Subject to the constraint of the perspective view of two parallel lines,the real parameters must locate on a constraint surface in the parameter space,and thereby the posterior mean estimation of the parameters can be obtained.Simulation results show that the method is effective.