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• W2165904679 abstract "The stable local classification of discrete surfaces with respect to features such as edges and corners or concave and convex regions, respectively, is as quite difficult as well as indispensable for many surface processing applications. Usually, the feature detection is done via a local curvature analysis. If concerned with large triangular and irregular grids, e.g., generated via a marching cube algorithm, the detectors are tedious to treat and a robust classification is hard to achieve. Here, a local classification method on surfaces is presented which avoids the evaluation of discretized curvature quantities. Moreover, it provides an indicator for smoothness of a given discrete surface and comes together with a built-in multiscale. The proposed classification tool is based on local zero and first moments on the discrete surface. The corresponding integral quantities are stable to compute and they give less noisy results compared to discrete curvature quantities. The stencil width for the integration of the moments turns out to be the scale parameter. Prospective surface processing applications are the segmentation on surfaces, surface comparison, and matching and surface modeling. Here, a method for feature preserving fairing of surfaces is discussed to underline the applicability of the presented approach." @default.
• W2165904679 created "2016-06-24" @default.
• W2165904679 creator A5024074640 @default.
• W2165904679 creator A5063603168 @default.
• W2165904679 creator A5067038712 @default.
• W2165904679 date "2004-09-01" @default.
• W2165904679 modified "2023-09-27" @default.
• W2165904679 title "Robust feature detection and local classification for surfaces based on moment analysis" @default.
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• W2165904679 doi "https://doi.org/10.1109/tvcg.2004.34" @default.
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