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24 November 2002 Signal processing approach to feature edge extraction in mesh surfaces
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Mesh surfaces are an important form of 3D object representation which has become increasingly significant in recent years due to evolving 3D acquisition techniques, computing power, and commercial needs. Feature edge extraction in polygonal meshes has numerous applications ranging from scene segmentation and object recognition to mesh compression. The method proposed in this paper for feature edge extraction in polygonal mesh surfaces follows the signal processing approach to edge detection in images at multiple scales and in the presence of noise. Namely, multiple smoothing iterations in the spatial domain are used to produce several versions of the original mesh at different scales, whereas the same topology is maintained throughout the scales. Edges are extracted at each scale by applying a differential operator to an underlying locally smooth surface represented by the mesh at that scale so as to evaluate the curvature at each vertex of the mesh. The proposed approach is based on the identification of multiple local support planes that facilitate the representation of an arbitrary mesh surface by a set of overlapping Monge patches. Such a representation enables the application of curvature estimation techniques commonly employed for range image analysis, and provides an alternative to Laplacian smoothing by local quadratic surface fitting. The proposed approach is suitable for irregular and non-dense meshes.
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Hoo-Nyong Jang and Gady Agam "Signal processing approach to feature edge extraction in mesh surfaces", Proc. SPIE 4794, Vision Geometry XI, (24 November 2002);

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