Name
Abstract | ||
|---|---|---|
In this article, a novel method to accurately estimate 3D surface of objects of interest is proposed. Each ray projected from 2D image plane to 3D space is modelled with the Gaussian kernel function. Then a mean shift algorithm with an annealing scheme is used to find maximums of the probability density function and recovers the 3D surface. Experimental results show that our method is more accurate to estimate 3D surface than the Radon transform-based approach. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICASSP.2010.5495474 | ICASSP |
Keywords | Field | DocType |
3d shape reconstruction,shape recognition,mean shift algorithm,probability density function,silhouettes,gaussian kernel function,3d shape estimation,2d image plane,gaussian processes,object detection,radon transform,mean shift,radon transforms,3d surface estimation,shape from silhouettes,3d shape recovery,probability,shape,magnetic resonance imaging,kernel,image reconstruction,surface reconstruction,computed tomography,histograms,estimation | Histogram,Surface reconstruction,Pattern recognition,Image plane,Artificial intelligence,Gaussian process,Mean-shift,Probability density function,Radon transform,Gaussian function,Mathematics | Conference |
ISSN | ISBN | Citations |
1520-6149 E-ISBN : 978-1-4244-4296-6 | 978-1-4244-4296-6 | 2 |
PageRank | References | Authors |
0.38 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Donghoon Kim | 1 | 51 | 8.42 |
| Jonathan Ruttle | 2 | 12 | 2.75 |
| Rozenn Dahyot | 3 | 340 | 32.62 |