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Our feature extraction is a multi-step refinement method, we use principle curvatures to flag potential feature points and develop a new approach to detect potential feature curves by employing our improved weight sensitive moving least squares. Then the potential feature points are enhanced by projecting onto the local potential feature curves. Using an optimized principal covariance analysis approach,...
We perform an effective algorithm for detecting characteristic curves on point sets. Our implication based on multi-step refinement operations: feature points are detected according to the biggish principal curvature of each point, and then enhance the points by projecting them onto their local nearest potential feature curves. The results indicate that our algorithm is sensitive to both sharp and...
We proposed a robust algorithm for valley-ridge extraction from point set. Our algorithm separately flag potential valley points and ridge points according to principle curvature of every point, then enhance the valley-ridge points by projecting them onto their local nearest potential valleys or ridges. Using an optimized principal covariance analysis approach, we smooth the projected points, finally...
Adaptively up-sampling of point-sampled models is one of key technologies to build multi-resolution point-based surfaces. In this paper, we propose an up-sampling algorithm for point-models. Our algorithm first defines a smooth manifold patch for each point in model based on a local projecting procedure, defined by the famous moving least squares (MLS) method. A valid up-sampling region is calculated...
This paper present an method for feature extraction from point set. Our algorithm use principle curvatures to ??ag potential feature points. Using an improved weight sensitive moving least squares, we developed a new approach to detect potential feature curves. The potential feature points are enhanced by projecting the points onto the local potential feature curves. Then smooth the projected points...
In field of numerical analysis, fitting points in 2D plane with a smooth curve is a widely investigated problem. In this paper, we propose a novel fitting method, which has ability of creating smooth curve approximating the points and filtering noises in the data. Our method is constructed based on the idea of blending local least squares fitting curves with radical weight function. The method first...
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