Practical non-parametric density estimation on a transformation group for vision
Practical non-parametric density estimation on a transformation group for vision
- Neue Suche nach: Miller, E.G.
- Neue Suche nach: Chefd'hotel, C.
- Neue Suche nach: Miller, E.G.
- Neue Suche nach: Chefd'hotel, C.
It is now common practice in machine vision to define the variability in an object's appearance in a factored manner, as a combination of shape and texture transformations. In this context, we present a simple and practical method for estimating non-parametric probability densities over a group of linear shape deformations. Samples drawn from such a distribution do not lie in a Euclidean space, and standard kernel density estimates may perform poorly. While variable kernel estimators may mitigate this problem to some extent, the geometry of the underlying configuration space ultimately demands a kernel, which accommodates its group structure. In this perspective, we propose a suitable invariant estimator on the linear group of non-singular matrices with positive determinant. We illustrate this approach by modeling image transformations in digit recognition problems, and present results showing the superiority of our estimator to comparable Euclidean estimators in this domain.
Dokumentinformationen
Practical non-parametric density estimation on a transformation group for vision
2003-01-01
404018 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Ähnliche Titel
Practical Non-parametric Density Estimation on a Transformation Group for Vision
| British Library Conference Proceedings | 2003
Parametric estimation of the cross-power spectral density
| Online Contents | 2005
Elusive dream or practical vision?
British Library Online Contents | 1994
Towards a Practical Stereo Vision Sensor
| IEEE | 2005
A Non-Parametric Approach for Independent Component Analysis Using Kernel Density Estimation
| British Library Conference Proceedings | 2004