发明名称 |
Online sparse matrix Gaussian process regression and visual applications |
摘要 |
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. |
申请公布号 |
US8190549(B2) |
申请公布日期 |
2012.05.29 |
申请号 |
US20080276128 |
申请日期 |
2008.11.21 |
申请人 |
YANG MING-HSUAN;RANGANATHAN ANANTH;HONDA MOTOR CO., LTD. |
发明人 |
YANG MING-HSUAN;RANGANATHAN ANANTH |
分类号 |
G06F17/00;G06N5/02 |
主分类号 |
G06F17/00 |
代理机构 |
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代理人 |
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地址 |
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