3D partial surface matching using differential geometry and statistical approaches
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 0302-9743, Vol: 7751 LNCS, Page: 172-180
2013
- 1Citations
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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- Citations1
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Conference Paper Description
3D partial surface matching approach is universal to 3D object recognition. In this paper, a new solution utilizing Gaussian curvature and mean curvature to represent the inherent structure of surface is proposed, Point-Pair Set is constructed by means of filtrating points with similar inherent characteristic in partial surface, then Triangle-Pair Set is demonstrated after locating 3D surface by asymmetry triangle skeleton and searching similar triangles in Point-Pair Set, finally, optimal transformation is illustrated by scoring function to transformations in Triangle-Pair Set and optimal matching is determined. Experiments show that the algorithm is suitable for 3D partial surface matching, and an encouraging matching efficiency, speed and running time complexity to irregular surfaces is introduced. © Springer-Verlag 2013.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84892947937&origin=inward; http://dx.doi.org/10.1007/978-3-642-36669-7_22; http://link.springer.com/10.1007/978-3-642-36669-7_22; http://link.springer.com/content/pdf/10.1007/978-3-642-36669-7_22; https://dx.doi.org/10.1007/978-3-642-36669-7_22; https://link.springer.com/chapter/10.1007/978-3-642-36669-7_22
Springer Science and Business Media LLC
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