Name
Abstract | ||
|---|---|---|
This paper extends the likelihood kernel density estimate of the visual hull proposed by Kim et al [1] by introducing a prior. Inference of the shape is performed using a meanshift algorithm over a posterior kernel density function that is refined iteratively using both a multiresolution framework (to avoid local maxima) and using KNN for selecting the best reconstruction basis at each iteration. This approach allows us to recover concave areas of the shape that are usually lost when estimating the visual hull. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-32436-9_7 | MUSCLE |
Keywords | Field | DocType |
likelihood kernel density estimate,best reconstruction basis,visual hull,meanshift algorithm,multiresolution framework,local maximum,posterior kernel density function,bayesian shape,concave area,knn,mean shift algorithm | Visual hull,Pattern recognition,Computer science,Inference,Maxima and minima,Artificial intelligence,Mean-shift,Variable kernel density estimation,Kernel density estimation,Bayesian probability | Conference |
Citations | PageRank | References |
2 | 0.50 | 15 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Donghoon Kim | 1 | 51 | 8.42 |
| Rozenn Dahyot | 2 | 340 | 32.62 |