TY - GEN
T1 - A Practical Guide to Multi-Image Alignment
AU - Aguerrebere, Cecilia
AU - Delbracio, Mauricio
AU - Bartesaghi, Alberto
AU - Sapiro, Guillermo
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - Multi-image alignment, bringing a group of images into common register, is an ubiquitous problem and the first step of many applications in a wide variety of domains. As a result, a great amount of effort is being invested in developing efficient multi-image alignment algorithms. Little has been done, however, to answer fundamental practical questions such as: what is the comparative performance of existing methods? is there still room for improvement? under which conditions should one technique be preferred over another? does adding more images or prior image information improve the registration results? In this work, we present a thorough analysis and evaluation of the main multi-image alignment methods which, combined with theoretical limits in multi-image alignment performance, allows us to organize them under a common framework and provide practical answers to these essential questions.
AB - Multi-image alignment, bringing a group of images into common register, is an ubiquitous problem and the first step of many applications in a wide variety of domains. As a result, a great amount of effort is being invested in developing efficient multi-image alignment algorithms. Little has been done, however, to answer fundamental practical questions such as: what is the comparative performance of existing methods? is there still room for improvement? under which conditions should one technique be preferred over another? does adding more images or prior image information improve the registration results? In this work, we present a thorough analysis and evaluation of the main multi-image alignment methods which, combined with theoretical limits in multi-image alignment performance, allows us to organize them under a common framework and provide practical answers to these essential questions.
KW - Bayesian estimators
KW - Multi-image alignment
UR - http://www.scopus.com/inward/record.url?scp=85054225185&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054225185&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2018.8461588
DO - 10.1109/ICASSP.2018.8461588
M3 - Conference contribution
AN - SCOPUS:85054225185
SN - 9781538646588
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1927
EP - 1931
BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Y2 - 15 April 2018 through 20 April 2018
ER -