Abstract
| Language | English |
|---|---|
| Title of host publication | 9th International Conference on Signal Processing, 2008 |
| Publisher | IEEE |
| Pages | 909-913 |
| Number of pages | 4 |
| ISBN (Print) | 978-1-4244-2178-7 |
| DOIs | |
| Publication status | Published - Oct 2008 |
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Keywords
- computerised tomography
- medical image processing
- object detection
- patient monitoring
- synthetic aperture radar
- computerized axial tomography
- copulas
- image change detection
- symmetrical Kullback-Leibler distance
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Image change detection using copulas. / Zeng, Xuexing; Durrani, T.S.
9th International Conference on Signal Processing, 2008. IEEE, 2008. p. 909-913.Research output: Chapter in Book/Report/Conference proceeding › Chapter
TY - CHAP
T1 - Image change detection using copulas
AU - Zeng, Xuexing
AU - Durrani, T.S.
PY - 2008/10
Y1 - 2008/10
N2 - This paper explores a new class of measures for the detection of changes in images, specially for images acquired from different classes of sensors such as synthetic aperture radar (SAR) systems or computerized axial tomography (CAT) systems, monitoring patients. The problems become very challenging as the local statistics may be different even though the observations in the images may be similar. By exploiting this similarity new approaches are proposed for change detection. Based on the assumption that some form of dependence exists between the images, this dependence can be modeled by copulas. By using the conditional copula and the second image to simulate the distribution of first image, the dependence between the two images may be more closely modeled by the ensuing joint distribution. As a follow on, the symmetrical Kullback-Leibler distance can be used to obtain the change indicator between the distributions associated with the two images. In this paper the conditional copula is used as a change detector and applied to scenes from two distinct and different image families -SAR and CAT, and its performance compared with that of conventional change detection algorithms, based on a pixel based difference measure and on local pixel statistics.
AB - This paper explores a new class of measures for the detection of changes in images, specially for images acquired from different classes of sensors such as synthetic aperture radar (SAR) systems or computerized axial tomography (CAT) systems, monitoring patients. The problems become very challenging as the local statistics may be different even though the observations in the images may be similar. By exploiting this similarity new approaches are proposed for change detection. Based on the assumption that some form of dependence exists between the images, this dependence can be modeled by copulas. By using the conditional copula and the second image to simulate the distribution of first image, the dependence between the two images may be more closely modeled by the ensuing joint distribution. As a follow on, the symmetrical Kullback-Leibler distance can be used to obtain the change indicator between the distributions associated with the two images. In this paper the conditional copula is used as a change detector and applied to scenes from two distinct and different image families -SAR and CAT, and its performance compared with that of conventional change detection algorithms, based on a pixel based difference measure and on local pixel statistics.
KW - computerised tomography
KW - medical image processing
KW - object detection
KW - patient monitoring
KW - synthetic aperture radar
KW - computerized axial tomography
KW - copulas
KW - image change detection
KW - symmetrical Kullback-Leibler distance
UR - http://dx.doi.org/10.1109/ICOSP.2008.4697275
U2 - 10.1109/ICOSP.2008.4697275
DO - 10.1109/ICOSP.2008.4697275
M3 - Chapter
SN - 978-1-4244-2178-7
SP - 909
EP - 913
BT - 9th International Conference on Signal Processing, 2008
PB - IEEE
ER -