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Efficient generation of discontinuity-preserving adaptive triangulations from range images
This paper presents an efficient technique for generating adaptive triangular meshes from range images. The algorithm consists of two stages. First, a user-defined number of points is adaptively sampled from the given range image. Those points are chosen by taking into account the surface shapes rep...
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Published in: | IEEE transactions on cybernetics 2004-10, Vol.34 (5), p.2003-2014 |
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container_end_page | 2014 |
container_issue | 5 |
container_start_page | 2003 |
container_title | IEEE transactions on cybernetics |
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creator | Garcia, M.A. Sappa, A.D. |
description | This paper presents an efficient technique for generating adaptive triangular meshes from range images. The algorithm consists of two stages. First, a user-defined number of points is adaptively sampled from the given range image. Those points are chosen by taking into account the surface shapes represented in the range image in such a way that points tend to group in areas of high curvature and to disperse in low-variation regions. This selection process is done through a noniterative, inherently parallel algorithm in order to gain efficiency. Once the image has been subsampled, the second stage applies a two and one half-dimensional Delaunay triangulation to obtain an initial triangular mesh. To favor the preservation of surface and orientation discontinuities (jump and crease edges) present in the original range image, the aforementioned triangular mesh is iteratively modified by applying an efficient edge flipping technique. Results with real range images show accurate triangular approximations of the given range images with low processing times. |
doi_str_mv | 10.1109/TSMCB.2004.832156 |
format | article |
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The algorithm consists of two stages. First, a user-defined number of points is adaptively sampled from the given range image. Those points are chosen by taking into account the surface shapes represented in the range image in such a way that points tend to group in areas of high curvature and to disperse in low-variation regions. This selection process is done through a noniterative, inherently parallel algorithm in order to gain efficiency. Once the image has been subsampled, the second stage applies a two and one half-dimensional Delaunay triangulation to obtain an initial triangular mesh. To favor the preservation of surface and orientation discontinuities (jump and crease edges) present in the original range image, the aforementioned triangular mesh is iteratively modified by applying an efficient edge flipping technique. 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The algorithm consists of two stages. First, a user-defined number of points is adaptively sampled from the given range image. Those points are chosen by taking into account the surface shapes represented in the range image in such a way that points tend to group in areas of high curvature and to disperse in low-variation regions. This selection process is done through a noniterative, inherently parallel algorithm in order to gain efficiency. Once the image has been subsampled, the second stage applies a two and one half-dimensional Delaunay triangulation to obtain an initial triangular mesh. To favor the preservation of surface and orientation discontinuities (jump and crease edges) present in the original range image, the aforementioned triangular mesh is iteratively modified by applying an efficient edge flipping technique. Results with real range images show accurate triangular approximations of the given range images with low processing times.</description><subject>Algorithms</subject><subject>Approximation</subject><subject>Artificial Intelligence</subject><subject>Computer vision</subject><subject>Curvature</subject><subject>Cybernetics</subject><subject>Discontinuity</subject><subject>Gain</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Image segmentation</subject><subject>Image sensors</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Intelligent robots</subject><subject>Mathematical analysis</subject><subject>Parallel algorithms</subject><subject>Pattern Recognition, Automated</subject><subject>Photogrammetry - methods</subject><subject>Pixel</subject><subject>Robot sensing systems</subject><subject>Robot vision systems</subject><subject>Shape</subject><subject>Studies</subject><subject>Two dimensional displays</subject><issn>1083-4419</issn><issn>2168-2267</issn><issn>1941-0492</issn><issn>2168-2275</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2004</creationdate><recordtype>article</recordtype><recordid>eNqNkU9PGzEQxa2qCCjwASoktOqh6mXDeP3_SCNaKgVxAC5cLGc9Gxkl3tTejcS3r0MiIfVQehpr_HujefMI-UxhQimYy4f72-n3SQPAJ5o1VMgP5JgaTmvgpvlY3qBZzTk1R-RTzs8AYMCoQ3JEhQDGjTwmT9ddF9qAcagWGDG5IfSx6rvKh9z2cQhxDMNLvU6YMW1CXFTOu_UQNlgNKbi4GJevklx1qV9VqXSwCiu3wHxKDjq3zHi2ryfk8cf1w_Smnt39_DW9mtUtZ2aosWlACa5Up7jmHUfBuHd-DojeOyeBg1RzgVx4pByLzdY5I4WfI2u15uyEfN3NXaf-94h5sKuyOy6XLmI_ZisVgCrO3wUbbRgwrf8DbJTSWhTw2z9BKhVlghV3Bf3yF_rcjymWw9hiAhopGSsQ3UFt6nNO2Nl1KsdML5aC3UZuXyO328jtLvKiudgPHucr9G-KfcYFON8BARHfvllZywj2B_uJsD0</recordid><startdate>20041001</startdate><enddate>20041001</enddate><creator>Garcia, M.A.</creator><creator>Sappa, A.D.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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The algorithm consists of two stages. First, a user-defined number of points is adaptively sampled from the given range image. Those points are chosen by taking into account the surface shapes represented in the range image in such a way that points tend to group in areas of high curvature and to disperse in low-variation regions. This selection process is done through a noniterative, inherently parallel algorithm in order to gain efficiency. Once the image has been subsampled, the second stage applies a two and one half-dimensional Delaunay triangulation to obtain an initial triangular mesh. To favor the preservation of surface and orientation discontinuities (jump and crease edges) present in the original range image, the aforementioned triangular mesh is iteratively modified by applying an efficient edge flipping technique. Results with real range images show accurate triangular approximations of the given range images with low processing times.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>15503496</pmid><doi>10.1109/TSMCB.2004.832156</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Approximation Artificial Intelligence Computer vision Curvature Cybernetics Discontinuity Gain Image Interpretation, Computer-Assisted - methods Image segmentation Image sensors Imaging, Three-Dimensional - methods Intelligent robots Mathematical analysis Parallel algorithms Pattern Recognition, Automated Photogrammetry - methods Pixel Robot sensing systems Robot vision systems Shape Studies Two dimensional displays |
title | Efficient generation of discontinuity-preserving adaptive triangulations from range images |
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