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Groupwise registration of aerial images

Arandjelovic, Ognjen, Pham, Duc-Son and Venkatesh, Svetha 2015, Groupwise registration of aerial images, in IJCAI 2015: Proceedings of the 24th International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, Palo Alto, Calif., pp. 2133-2139.

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Title Groupwise registration of aerial images
Author(s) Arandjelovic, Ognjen
Pham, Duc-Son
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name International Joint Conference on Artificial Intelligence (24th : 2015 : Buenos Aires, Argentina)
Conference location Buenos Aires, Argentina
Conference dates 25-31 Jul. 2015
Title of proceedings IJCAI 2015: Proceedings of the 24th International Joint Conference on Artificial Intelligence
Editor(s) Yang, Qiang
Wooldridge, Michael
Publication date 2015
Start page 2133
End page 2139
Total pages 7
Publisher International Joint Conferences on Artificial Intelligence
Place of publication Palo Alto, Calif.
Summary This paper addresses the task of time separated aerial image registration. The ability to solve this problem accurately and reliably is important for a variety of subsequent image understanding applications. The principal challenge lies in the extent and nature of transient appearance variation that a land area can undergo, such as that caused by the change in illumination conditions, seasonal variations, or the occlusion by non-persistent objects (people, cars). Our work introduces several novelties: (i) unlike all previous work on aerial image registration, we approach the problem using a set-based paradigm; (ii) we show how local, pairwise constraints can be used to enforce a globally good registration using a constraints graph structure; (iii) we show how a simple holistic representation derived from raw aerial images can be used as a basic building block of the constraints graph in a manner which achieves both high registration accuracy and speed. We demonstrate: (i) that the proposed method outperforms the state-of-the-art for pair-wise registration already, achieving greater accuracy and reliability, while at the same time reducing the computational cost of the task; and (ii) that the increase in the number of available images in a set consistently reduces the average registration error.
ISBN 9781577357384
ISSN 1045-0823
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, International Joint Conferences on Artificial Intelligence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082742

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