Heteroscedastic Regression in Computer Vision: Problems with Bilinear Constraint

  title={Heteroscedastic Regression in Computer Vision: Problems with Bilinear Constraint},
  author={Yoram Leedan and Peter Meer},
  journal={International Journal of Computer Vision},
We present an algorithm to estimate the parameters of a linear model in the presence of heteroscedastic noise, i.e., each data point having a different covariance matrix. The algorithm is motivated by the recovery of bilinear forms, one of the fundamental problems in computer vision which appears whenever the epipolar constraint is imposed, or a conic is fit to noisy data points. We employ the errors-in-variables (EIV) model and show why already at moderate noise levels most available methods… CONTINUE READING
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