Bayesian Formulation of Image Patch Matching Using Crosscorrelation
(2012) In Journal of Mathematical Imaging and Vision 43(1). p.7287 Abstract (Swedish)
 Abstract in Undetermined
A classical solution for matching two image patches is to use the crosscorrelation coefficient. This works well if there is a lot of structure within the patches, but not so well if the patches are close to uniform. This means that some patches are matched with more confidence than others. By estimating this uncertainty, more weight can be put on the confident matches than those that are more uncertain. To enable this two distribution functions for two different cases are used: (i) the correlation between two patches showing the same object but with different lighting conditions and different noise realisations and (ii) the correlation between two unrelated patches. Using these two distributions... (More)  Abstract in Undetermined
A classical solution for matching two image patches is to use the crosscorrelation coefficient. This works well if there is a lot of structure within the patches, but not so well if the patches are close to uniform. This means that some patches are matched with more confidence than others. By estimating this uncertainty, more weight can be put on the confident matches than those that are more uncertain. To enable this two distribution functions for two different cases are used: (i) the correlation between two patches showing the same object but with different lighting conditions and different noise realisations and (ii) the correlation between two unrelated patches. Using these two distributions the patch matching problem is, in this paper, formulated as a binary classification problem. The probability of two patches matching is derived. The model depends on the signal to noise ratio. The noise level is reasonably invariant over time, while the signal level, represented by the amount of structure in the patch or its spatial variance, has to be measured for every frame. A common application where this is useful is feature point matching between different images. Another application is background/foreground segmentation. This paper will concentrate on the latter application. It is shown how the theory can be used to implement a very fast background/foreground segmentation algorithm by transforming the calculations to the DCTdomain and processing a motionJPEG stream without uncompressing it. This allows the algorithm to be embedded on a 150 MHz ARM based network camera. It is also suggested to use recursive quantile estimation to estimate the background model. This gives very accurate background models even if there is a lot of foreground present during the initialisation of the model. (Less)  Abstract
 A classical solution for matching two image patches is to use the crosscorrelation coefficient. This works well if there is a lot of structure within the patches, but not so well if the patches are close to uniform. This means that some patches are matched with more confidence than others. By estimating this uncertainty, more weight can be put on the confident matches than those that are more uncertain. To enable this two distribution functions for two different cases are used: (i) the correlation between two patches showing the same object but with different lighting conditions and different noise realisations and (ii) the correlation between two unrelated patches.
Using these two distributions the patch matching problem is, in... (More)  A classical solution for matching two image patches is to use the crosscorrelation coefficient. This works well if there is a lot of structure within the patches, but not so well if the patches are close to uniform. This means that some patches are matched with more confidence than others. By estimating this uncertainty, more weight can be put on the confident matches than those that are more uncertain. To enable this two distribution functions for two different cases are used: (i) the correlation between two patches showing the same object but with different lighting conditions and different noise realisations and (ii) the correlation between two unrelated patches.
Using these two distributions the patch matching problem is, in this paper, formulated as a binary classification problem. The probability of two patches matching is derived. The model depends on the signal to noise ratio. The noise level is reasonably invariant over time, while the signal level, represented by the amount of structure in the patch or its spatial variance, has to be measured for every frame.
A common application where this is useful is feature point matching between different images. Another application is background/foreground segmentation. This paper will concentrate on the latter application. It is shown how the theory can be used to implement a very fast background/foreground segmentation algorithm by transforming the calculations to the DCTdomain and processing a motionJPEG stream without uncompressing it. This allows the algorithm to be embedded on a 150 MHz ARM based network camera. It is also suggested to use recursive quantile estimation to estimate the background model. This gives very accurate background models even if there is a lot of foreground present during the initialisation of the model. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/record/2295601
 author
 Ardö, Håkan ^{LU} and Åström, Karl ^{LU}
 organization
 publishing date
 2012
 type
 Contribution to journal
 publication status
 published
 subject
 keywords
 Patchmatching · Lighting variations ·Background/Foregroundsegmentation · Bayesianclassification
 in
 Journal of Mathematical Imaging and Vision
 volume
 43
 issue
 1
 pages
 72  87
 publisher
 Springer
 external identifiers

 wos:000302346000006
 scopus:84859434902
 ISSN
 09249907
 DOI
 10.1007/s108510110287x
 language
 English
 LU publication?
 yes
 id
 34babca648644057ba31bec1f39a7f2b (old id 2295601)
 date added to LUP
 20120127 19:05:02
 date last changed
 20190220 02:39:31
@article{34babca648644057ba31bec1f39a7f2b, abstract = {A classical solution for matching two image patches is to use the crosscorrelation coefficient. This works well if there is a lot of structure within the patches, but not so well if the patches are close to uniform. This means that some patches are matched with more confidence than others. By estimating this uncertainty, more weight can be put on the confident matches than those that are more uncertain. To enable this two distribution functions for two different cases are used: (i) the correlation between two patches showing the same object but with different lighting conditions and different noise realisations and (ii) the correlation between two unrelated patches.<br/><br/>Using these two distributions the patch matching problem is, in this paper, formulated as a binary classification problem. The probability of two patches matching is derived. The model depends on the signal to noise ratio. The noise level is reasonably invariant over time, while the signal level, represented by the amount of structure in the patch or its spatial variance, has to be measured for every frame.<br/><br/>A common application where this is useful is feature point matching between different images. Another application is background/foreground segmentation. This paper will concentrate on the latter application. It is shown how the theory can be used to implement a very fast background/foreground segmentation algorithm by transforming the calculations to the DCTdomain and processing a motionJPEG stream without uncompressing it. This allows the algorithm to be embedded on a 150 MHz ARM based network camera. It is also suggested to use recursive quantile estimation to estimate the background model. This gives very accurate background models even if there is a lot of foreground present during the initialisation of the model.}, author = {Ardö, Håkan and Åström, Karl}, issn = {09249907}, keyword = {Patchmatching · Lighting variations ·Background/Foregroundsegmentation · Bayesianclassification}, language = {eng}, number = {1}, pages = {7287}, publisher = {Springer}, series = {Journal of Mathematical Imaging and Vision}, title = {Bayesian Formulation of Image Patch Matching Using Crosscorrelation}, url = {http://dx.doi.org/10.1007/s108510110287x}, volume = {43}, year = {2012}, }