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International Conference on Computer Vision Systems

ICVS 2015: Computer Vision Systems pp 91–103Cite as

Bayesian Formulation of Gradient Orientation Matching

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9163))

Abstract

Gradient orientations are a common feature used in many computer vision algorithms. It is a good feature when the gradient magnitudes are high, but can be very noisy when the magnitudes are low. This means that some gradient orientations are matched with more confidence than others. By estimating this uncertainty, more weight can be put on the confident matches than those with higher uncertainty. To enable this, we derive the probability distribution of gradient orientations based on a signal to noise ratio defined as the gradient magnitude divided by the standard deviation of the Gaussian noise. The noise level is reasonably invariant over time, while the magnitude, has to be measured for every frame. Using this probability distribution we formulate the matching of gradient orientations as a Bayesian classification problem.

A common application where this is useful is feature point matching. Another application is background/foreground segmentation. This paper will use the latter application as an example, but is focused on the general formulation. It is shown how the theory can be used to implement a very fast background/foreground segmentation algorithm that is capable of handling complex lighting variations.

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Notes

  1. 1.

    http://research.microsoft.com/users/jckrumm/WallFlower/TestImages.htm.

  2. 2.

    http://www.changedetection.net/.

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Author information

Authors and Affiliations

  1. Centre for Mathematical Sciences, Lund University, Lund, Sweden

    Håkan Ardö & Linus Svärm

Authors
  1. Håkan Ardö
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  2. Linus Svärm
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Corresponding author

Correspondence to Håkan Ardö .

Editor information

Editors and Affiliations

  1. Aalborg University, Copenhagen, Denmark

    Lazaros Nalpantidis

  2. Aalborg University, Copenhagen, Denmark

    Volker Krüger

  3. Royal Institute of Technology - KTH, Stockholm, Sweden

    Jan-Olof Eklundh

  4. Democritus University of Thrace, Xanthi, Greece

    Antonios Gasteratos

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© 2015 Springer International Publishing Switzerland

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Ardö, H., Svärm, L. (2015). Bayesian Formulation of Gradient Orientation Matching. In: Nalpantidis, L., Krüger, V., Eklundh, JO., Gasteratos, A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science(), vol 9163. Springer, Cham. https://doi.org/10.1007/978-3-319-20904-3_9

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  • DOI: https://doi.org/10.1007/978-3-319-20904-3_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20903-6

  • Online ISBN: 978-3-319-20904-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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