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Robust Error Metric Analysis for Noise Estimation in Image Indexing

9 years 27 days ago
Robust Error Metric Analysis for Noise Estimation in Image Indexing
In many computer vision algorithms, the well known Euclidean or SSD (sum of the squared differences) metric is prevalent and justified from a maximum likelihood perspective when the additive noise is Gaussian. However, Gaussian noise distribution assumption is often invalid. Previous research has found that other metrics such as double exponential metric or Cauchy metric provide better results, in accordance with the maximum likelihood approach. In this paper, we examine different error metrics and provide a general guideline to derive a rich set of nonlinear estimations. Our results on image databases show more robust results are obtained for noise estimation based on the proposed error metric analysis.
Qi Tian, Jie Yu, Qing Xue, Nicu Sebe, Thomas S. Hu
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2004
Where CVPR
Authors Qi Tian, Jie Yu, Qing Xue, Nicu Sebe, Thomas S. Huang
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