Efficient and accurate set-based registration of time-separated aerial images Arandjelovic, Ognjen, Pham, Duc-Son and Venkatesh, Svetha 2015, Efficient and accurate set-based registration of time-separated aerial images, Pattern recognition, vol. 48, no. 11, pp. 3466-3476, doi: 10.1016/j.patcog.2015.04.011. Attached Files Name Description MIMEType Size Downloads Title Efficient and accurate set-based registration of time-separated aerial images Author(s) Arandjelovic, Ognjen Pham, Duc-Son Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Journal name Pattern recognition Volume number 48 Issue number 11 Start page 3466 End page 3476 Total pages 11 Publisher Elsevier Place of publication Amsterdam, The Netherlands Publication date 2015-11 ISSN 0031-3203 1873-5142 Keyword(s) Alignment Constraints Graph Map Remote Sensing Science & Technology Technology Computer Science, Artificial Intelligence Engineering, Electrical & Electronic Computer Science Engineering PERFORMANCE EVALUATION SUBPIXEL REGISTRATION FACE RECOGNITION EXTRACTION INVARIANT FEATURES VIDEO MODEL 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 under illumination conditions, seasonal variations, or the occlusion by non-persistent objects (people, cars). Our work introduces several major novelties (i) unlike previous work on aerial image registration, we approach the problem using a set-based paradigm; (ii) we show how image space local, pair-wise 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; (iv) lastly, we introduce a new and, to the best of our knowledge, the only data corpus suitable for the evaluation of set-based aerial image registration algorithms. Using this data set, 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, with a major difference already for a single additional image. Language eng DOI 10.1016/j.patcog.2015.04.011 Field of Research 080109 Pattern Recognition and Data Mining 0899 Other Information And Computing Sciences 0906 Electrical And Electronic Engineering 0801 Artificial Intelligence And Image Processing Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category C1 Refereed article in a scholarly journal ERA Research output type C Journal article Copyright notice ©2015, Elsevier Persistent URL http://hdl.handle.net/10536/DRO/DU:30077470 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Centre for Pattern Recognition and Data Analytics (PRADA) 2018 ERA Submission Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 31 Aug 2015, 13:36:44 EST Tue, 01 Sep 2015, 05:03:06 EST Wed, 14 Oct 2015, 14:33:01 EST Tue, 01 Mar 2016, 15:41:05 EST Wed, 02 Mar 2016, 05:07:09 EST Fri, 09 Mar 2018, 13:08:53 EST Fri, 09 Mar 2018, 13:49:55 EST Fri, 09 Mar 2018, 13:55:44 EST Fri, 09 Mar 2018, 14:10:27 EST Mon, 12 Mar 2018, 07:21:19 EST Mon, 12 Mar 2018, 07:22:06 EST Filtered Full Citation counts:   Cited 12 times in TR Web of Science Cited 15 times in Scopus Search Google Scholar Access Statistics: 276 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Fri, 09 Mar 2018, 13:08:53 EST

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