DocumentCode :
2219550
Title :
Reconstruction of objects with jagged edges through Rao-Blackwellized fitting of piecewise smooth subdivision curves
Author :
Kaess, Michael ; Dellaert, Frank
Author_Institution :
Coll. of Comput., Georgia Inst. of Technol., Atlanta, GA, USA
fYear :
2003
fDate :
17-17 Oct. 2003
Firstpage :
39
Lastpage :
47
Abstract :
In some applications objects are known to have non-smooth or "jagged" edges, which are not well approximated by smooth curves. We use subdivision curves as a simple but flexible curve representation, which allows tagging corners to model non-smooth features along otherwise smooth curves. A Markov chain Monte Carlo approach yields an approximate posterior distribution over tags, while Rao-Blackwellization allows us to integrate out the control point locations by an approximation. We apply this general methodology to multi-view reconstruction of piecewise smooth curves from multiple calibrated views in which the object has been segmented from the background. Results are shown for multiple images of two pot shards as would be encountered in archaeological applications.
Keywords :
Markov processes; Monte Carlo methods; archaeology; computer graphics; curve fitting; image reconstruction; Markov chain approach; Monte Carlo approach; Rao-Blackwellization; Rao-Blackwellized fitting; archaeological application; curve representation; image reconstruction; jagged edges; multiview reconstruction; object reconstruction; piecewise smooth curves; piecewise smooth subdivision curve fitting; smooth subdivision curves; Curve fitting; Image reconstruction; Monte Carlo methods; Polynomials; Reconstruction algorithms; Sampling methods; Spline; Surface fitting; Surface reconstruction; Tagging;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Higher-Level Knowledge in 3D Modeling and Motion Analysis, 2003. HLK 2003. First IEEE International Workshop on
Conference_Location :
Nice, France
Print_ISBN :
0-7695-2049-9
Type :
conf
DOI :
10.1109/HLK.2003.1240857
Filename :
1240857
Link To Document :
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