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Bias in Shape Estimation

  • Hui Ji
  • Cornelia Fermüller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3023)

Abstract

This paper analyses the uncertainty in the estimation of shape from motion and stereo. It is shown that there are computational limitations of a statistical nature that previously have not been recognized. Because there is noise in all the input parameters, we cannot avoid bias. The analysis rests on a new constraint which relates image lines and rotation to shape. Because the human visual system has to cope with bias as well, it makes errors. This explains the underestimation of slant found in computational and psychophysical experiments, and demonstrated here for an illusory display. We discuss properties of the best known estimators with regard to the problem, as well as possible avenues for visual systems to deal with the bias.

Keywords

Sensor Noise Psychophysical Experiment Shape Estimation Camera Center Texture Plane 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Hui Ji
    • 1
  • Cornelia Fermüller
    • 1
  1. 1.Center for Automation ResearchUniversity of MarylandCollege ParkUSA

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