Name
Abstract | ||
|---|---|---|
Recovering the shape of a class of objects requires establishing correct correspondences between manually or automatically annotated landmark points. In this study, we utilise a novel approach to automatically recover the shape of hand outlines from a series of 2D training images. Automated landmark extraction is accomplished through the use of the self-organising model the growing neural gas (GNG) network which is able to learn and preserve the topological relations of a given set of input patterns without requiring a priori knowledge of the structure of the input space. To measure the quality of the mapping throughout the adaptation process we use the topographic product. Results are given for the training set of hand outlines. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11744023_25 | ECCV |
Keywords | DocType | Volume |
input pattern,novel approach,self-organising model,input space,training image,annotated landmark point,topology preservation model,neural gas,correct correspondence,automated landmark extraction,adaptation process,a priori knowledge | Conference | 3951 |
ISSN | ISBN | Citations |
0302-9743 | 3-540-33832-2 | 8 |
PageRank | References | Authors |
0.60 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anastassia Angelopoulou | 1 | 102 | 21.29 |
| José Garcia Rodriguez | 2 | 55 | 9.71 |
| Alexandra Psarrou | 3 | 199 | 27.14 |