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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2020.2984897
|2 doi
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|a eng
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| 100 |
1 |
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|a Zhang, Xinxin
|e verfasserin
|4 aut
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| 245 |
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|a All-pass Parametric Image Registration
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| 264 |
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|c 2020
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 27.02.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a Image registration is a required step in many practical applications that involve the acquisition of multiple related images. In this paper, we propose a methodology to deal with both the geometric and intensity transformations in the image registration problem. The main idea is to modify an accurate and fast elastic registration algorithm (Local All-Pass-LAP) so that it returns a parametric displacement field, and to estimate the intensity changes by fitting another parametric expression. Although we demonstrate the methodology using a low-order parametric model, our approach is highly flexible and easily allows substantially richer parametrisations, while requiring only limited extra computation cost. In addition, we propose two novel quantitative criteria to evaluate the accuracy of the alignment of two images ("salience correlation") and the number of degrees of freedom ("parsimony") of a displacement field, respectively. Experimental results on both synthetic and real images demonstrate the high accuracy and computational efficiency of our methodology. Furthermore, we demonstrate that the resulting displacement fields are more parsimonious than the ones obtained in other state-of-the-art image registration approaches
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|a Journal Article
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| 700 |
1 |
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|a Gilliam, Christopher
|e verfasserin
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| 700 |
1 |
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|a Blu, Thierry
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g (2020) vom: 07. Apr.
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|u http://dx.doi.org/10.1109/TIP.2020.2984897
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