Name
Abstract | ||
|---|---|---|
One of the challenges in shape tracking is how to deal with associating measurements to sources in the shape, while also taking to account parameters such as shape curvature and noise characteristics. Partial Information Models (PIMs) introduce a new approach that addresses this issue. The idea is to reparametrize each measurement into two components, one which depends on the position of its source on the shape, and another which depends on how well it fits in the shape. This allows for the derivation of a partial likelihood which combines the strengths of probabilistic approaches and distance minimization techniques. We propose an implementation of PIMs using level-sets, which allow for a close approximation of the distribution of distances we expect for a given shape. In turn, this can be used to develop estimators that are highly robust against high noise and occlusions. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/MFI.2015.7295736 | 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) |
Keywords | Field | DocType |
shape tracking,partial information models,PIMs,shape curvature,probabilistic approaches,distance minimization techniques | Computer science,Minification,Shape optimization,Artificial intelligence,Probabilistic logic,Computer vision,Active shape model,Curvature,Pattern recognition,Heat kernel signature,Machine learning,Shape analysis (digital geometry),Estimator | Conference |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonio Zea | 1 | 41 | 5.25 |
| Florian Faion | 2 | 74 | 7.95 |
| Uwe D. Hanebeck | 3 | 944 | 133.52 |