2013
DOI: 10.1080/17452759.2013.866874
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Automatic prismatic feature segmentation of scanning-derived meshes utilising mean curvature histograms

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Cited by 4 publications
(4 citation statements)
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“…To robustly detect the primitive features, an existing curvature-based feature segmentation method (Chen and Feng 2014) is used first. After segmenting the mesh model into various feature patches, the shape of each segmented patch and their parameters can be reliably identified by analysing the signs of its average mean and average Gaussian curvatures (Chen and Feng, 2015).…”
Section: Model Preparationmentioning
confidence: 99%
“…To robustly detect the primitive features, an existing curvature-based feature segmentation method (Chen and Feng 2014) is used first. After segmenting the mesh model into various feature patches, the shape of each segmented patch and their parameters can be reliably identified by analysing the signs of its average mean and average Gaussian curvatures (Chen and Feng, 2015).…”
Section: Model Preparationmentioning
confidence: 99%
“…For example, these distributions can be used in image processing to improve compression [10]. Some methods in 3D mesh processing also begin to use distributions, for example to apply segmentation [4] or extract object edges [6].…”
Section: Distributionsmentioning
confidence: 99%
“…In our case, the best results are reached using curvatures, because in reverse engineering, features are extracted from curvature analysis. Some methods use curvatures to segment by discontinuities [3,4] or clustering [7], but are often not robust enough around object edges or are designed for CAD meshes [9,1]. Indeed, curvatures are often wrong around object edges because adjacent points can run over many different features.…”
Section: Segmentationmentioning
confidence: 99%
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