Name
Abstract | ||
|---|---|---|
This work considers the problem of estimating the parameters of an extended object based on noisy point observations from its boundary. The intention is to explore relationships between common approaches by breaking them down into their basic assumptions within the Bayesian framework. In doing so, we find that distance-minimizing curve fitting algorithms can be modeled by using a special Spatial Distribution Model, where the source distribution is approximated by a greedy one-to-one association of points to sources on the shape boundary. Based on this insight, we explore the origin of the estimation bias, which is a well-known issue of curve fitting algorithms. Furthermore, we derive a general scheme to alleviate its effect for arbitrary shapes, as well as for non-isotropic noise. This procedure is shown to be a generalization of related special solutions. |
Year | Venue | Keywords |
|---|---|---|
2014 | Fusion | bayesian shape estimation,linear regression kalman filter,parameter estimation,bayes methods,bias reduction,curve fitting,spatial distribution model,extended object,non-isotropic noise,greedy one-to-one association,shape boundary,noisy point observations,nonisotropic noise,source distribution,distance-minimizing curve fitting,shape fitting,special spatial distribution model,arbitrary shapes,bayesian framework,graphical models,distribution functions,shape,noise,estimation,noise measurement |
Field | DocType | Citations |
Curve fitting,Pattern recognition,Computer science,Shape of the distribution,Bayesian linear regression,Algorithm,Kalman filter,Artificial intelligence,Shape fitting,Machine learning,Bayesian probability,Linear regression | Conference | 3 |
PageRank | References | Authors |
0.45 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Florian Faion | 1 | 74 | 7.95 |
| Antonio Zea | 2 | 41 | 5.25 |
| Uwe D. Hanebeck | 3 | 944 | 133.52 |