Name
Abstract | ||
|---|---|---|
Tracking of articulated objects is a challenging task in Computer Vision. A highly target specific model can improve the robustness of the tracking by eliminating or reducing the ambiguities in the association task. This paper presents a flexible framework, which allows to build target specific, part-based models for arbitrary articulated objects. The rigid parts are described by hierarchical spring systems in form of attributed graph pyramids and connected via articulation points, which transfer position information between the adjacent parts. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-03767-2_123 | CAIP |
Keywords | Field | DocType |
computer vision | Computer vision,Graph,Pattern recognition,Computer science,Robustness (computer science),Spring system,Artificial intelligence,Articulated body pose estimation | Conference |
Volume | ISSN | Citations |
5702 | 0302-9743 | 2 |
PageRank | References | Authors |
0.39 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicole M. Artner | 1 | 78 | 9.04 |
| Adrian Ion | 2 | 222 | 21.11 |
| Walter G. Kropatsch | 3 | 896 | 152.91 |