Bayesian Formulation of Gradient Orientation Matching
(2015) 10th International Conference on Computer Vision Systems (ICVS) In Lecture Notes in Computer Science 9163. p.91103 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)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/record/8398076
 author
 Ardö, Håkan ^{LU} and Svärm, Linus ^{LU}
 organization
 publishing date
 2015
 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
 16113349
 03029743
 DOI
 10.1007/9783319209043_9
 language
 English
 LU publication?
 yes
 id
 9b76da743e3d401980c62c35306ce88c (old id 8398076)
 date added to LUP
 20151221 09:36:18
 date last changed
 20180312 20:31:30
@inproceedings{9b76da743e3d401980c62c35306ce88c, 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 = {16113349}, language = {eng}, pages = {91103}, publisher = {Springer}, title = {Bayesian Formulation of Gradient Orientation Matching}, url = {http://dx.doi.org/10.1007/9783319209043_9}, volume = {9163}, year = {2015}, }