Online sparse matrix Gaussian process regression and visual applications
US8190549B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 21, 2008 |
| Grant date | May 29, 2012 |
| Priority date | — |
| Expiry date | Feb 3, 2031 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/764
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
An online sparse matrix Gaussian process (OSMGP) uses online updates to provide an accurate and efficient regression for applications such as pose estimation and object tracking. A regression calculation module calculates a regression on a sequence of input images to generate output predictions based on a learned regression model. The regression model is efficiently updated by representing a covariance matrix of the regression model using a sparse matrix factor (e.g., a Cholesky factor). The sparse matrix factor is maintained and updated in real-time based on the output predictions. Hyperparameter optimization, variable reordering, and matrix downdating techniques can also be applied to further improve the accuracy and/or efficiency of the regression process.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.