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  • Improving Phase Correlation for Image Registration

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    74188_1.pdf (1.465Mb)
    Author(s)
    Gonzalez, Ruben
    Griffith University Author(s)
    Gonzalez, Ruben
    Year published
    2011
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    Abstract
    Phase correlation is a well-known technique for image registration that is robust to noise and operates in constant time. Scale and rotation invariance can be achieved through means of a log-polar transformation. Unfortunately this method has been historically shown to be unable to handle large rotation or scaling factors, making it unsuitable for many image registration tasks. This paper presents a novel phase correlation based technique that is shown to outperform the current state of the art image registration methods in terms of being able to recover larger rotation and scaling factors and with reduced computational ...
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    Phase correlation is a well-known technique for image registration that is robust to noise and operates in constant time. Scale and rotation invariance can be achieved through means of a log-polar transformation. Unfortunately this method has been historically shown to be unable to handle large rotation or scaling factors, making it unsuitable for many image registration tasks. This paper presents a novel phase correlation based technique that is shown to outperform the current state of the art image registration methods in terms of being able to recover larger rotation and scaling factors and with reduced computational requirements.
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    Conference Title
    Proceedings of Image and Vision Computing New Zealand 2011
    Publisher URI
    http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=17867&..
    Copyright Statement
    © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Computer Vision
    Publication URI
    http://hdl.handle.net/10072/44512
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    • Conference outputs

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