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Bayesian Formulation of Gradient Orientation Matching

Ardö, Håkan LU and Svärm, Linus LU (2015) 10th International Conference on Computer Vision Systems (ICVS) In Lecture Notes in Computer Science 9163. p.91-103
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... (More)
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. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
Lecture Notes in Computer Science
volume
9163
pages
91 - 103
publisher
Springer
conference name
10th International Conference on Computer Vision Systems (ICVS)
external identifiers
  • wos:000364183300009
  • scopus:84948963992
ISSN
0302-9743
1611-3349
DOI
10.1007/978-3-319-20904-3_9
language
English
LU publication?
yes
id
9b76da74-3e3d-4019-80c6-2c35306ce88c (old id 8398076)
date added to LUP
2015-12-21 09:36:18
date last changed
2017-01-01 03:16:53
@inproceedings{9b76da74-3e3d-4019-80c6-2c35306ce88c,
  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.},
  author       = {Ardö, Håkan and Svärm, Linus},
  booktitle    = {Lecture Notes in Computer Science},
  issn         = {0302-9743},
  language     = {eng},
  pages        = {91--103},
  publisher    = {Springer},
  title        = {Bayesian Formulation of Gradient Orientation Matching},
  url          = {http://dx.doi.org/10.1007/978-3-319-20904-3_9},
  volume       = {9163},
  year         = {2015},
}