TY - GEN
T1 - Image registration by minimization of residual complexity
AU - Myronenko, Andriy
AU - Song, Xubo
PY - 2009
Y1 - 2009
N2 - Accurate definition of similarity measure is a key component in image registration. Most commonly used intensitybased similarity measures rely on the assumptions of independence and stationarity of the intensities from pixel to pixel. Such measures cannot capture the complex interactions among the pixel intensities, and often result in less satisfactory registration performances, especially in the presence of nonstationary intensity distortions. We propose a novel similarity measure that accounts for intensity nonstationarities and complex spatially-varying intensity distortions. We derive the similarity measure by analytically solving for the intensity correction field and its adaptive regularization. The final measure can be interpreted as one that favors a registration with minimum compression complexity of the residual image between the two registered images. This measure produces accurate registration results on both artificial and real-world problems that we have tested, whereas many other state-of-the-art similarity measures have failed to do so.
AB - Accurate definition of similarity measure is a key component in image registration. Most commonly used intensitybased similarity measures rely on the assumptions of independence and stationarity of the intensities from pixel to pixel. Such measures cannot capture the complex interactions among the pixel intensities, and often result in less satisfactory registration performances, especially in the presence of nonstationary intensity distortions. We propose a novel similarity measure that accounts for intensity nonstationarities and complex spatially-varying intensity distortions. We derive the similarity measure by analytically solving for the intensity correction field and its adaptive regularization. The final measure can be interpreted as one that favors a registration with minimum compression complexity of the residual image between the two registered images. This measure produces accurate registration results on both artificial and real-world problems that we have tested, whereas many other state-of-the-art similarity measures have failed to do so.
UR - http://www.scopus.com/inward/record.url?scp=70450194988&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70450194988&partnerID=8YFLogxK
U2 - 10.1109/CVPRW.2009.5206571
DO - 10.1109/CVPRW.2009.5206571
M3 - Conference contribution
AN - SCOPUS:70450194988
SN - 9781424439935
T3 - 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009
SP - 49
EP - 56
BT - 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009
PB - IEEE Computer Society
T2 - 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009
Y2 - 20 June 2009 through 25 June 2009
ER -