Groupwise registration of aerial images
Ognjen Arandjelovic, Duc-Son Pham, Svetha Venkatesh

TL;DR
This paper introduces a novel set-based aerial image registration method that leverages local pair-wise constraints and holistic image representations to improve accuracy, reliability, and computational efficiency over existing techniques.
Contribution
The work presents a unique set-based registration approach using a constraints graph and holistic image features, outperforming prior pair-wise methods in accuracy and speed.
Findings
Outperforms state-of-the-art pair-wise registration methods
Achieves higher accuracy and reliability
Reduces computational cost
Abstract
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, 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…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Robotics and Sensor-Based Localization · Advanced Neural Network Applications
