Wavelet-based image registration

Paulson, Christopher; Ezekiel, Soundararajan; Wu, Dapeng

doi:10.1117/12.851210

Cited by 15 publications

(9 citation statements)

References 22 publications

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“…Finally, we would like to point out that the algorithm of Davis The results of Task 7 were published in Ref. [76].…”

Section: Resultsmentioning

confidence: 99%

Advanced Techniques for Scene Analysis

Wu¹

2010

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“…Finally, we would like to point out that the algorithm of Davis The results of Task 7 were published in Ref. [76].…”

Section: Resultsmentioning

confidence: 99%

Advanced Techniques for Scene Analysis

Wu¹

2010

Self Cite

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“…It was then extended to the wavelet transform by Djamdji et al (1993) and Zheng & Chellappa (1993). We refer to Zitová & Flusser (2003) and Paulson et al (2010) for a review on the different techniques developed in this area. However, none of theses algorithms can be directly applied for our purpose.…”

Section: Multiscale Cross-correlationmentioning

confidence: 99%

“…Djamdji et al 1993;Zheng & Chellappa 1993;Adams & Williams 2003;Zitová & Flusser 2003;Paulson et al 2010). Structural patterns observed in astronomical images often do not have a defined or even preferred shape, which is an aspect relied upon in a number of the existing object recognition algorithms (e.g., Agarwal et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Wavelet-based decomposition and analysis of structural patterns in astronomical images

Mertens

Lobanov

2015

A&A

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Context. Images of spatially resolved astrophysical objects contain a wealth of morphological and dynamical information, and effectively extracting this information is of paramount importance for understanding the physics and evolution of these objects. The algorithms and methods currently employed for this purpose (such as Gaussian model fitting) often use simplified approaches to describe the structure of resolved objects. Aims. Automated (unsupervised) methods for structure decomposition and tracking of structural patterns are needed for this purpose to be able to treat the complexity of structure and large amounts of data involved. Methods. We developed a new wavelet-based image segmentation and evaluation (WISE) method for multiscale decomposition, segmentation, and tracking of structural patterns in astronomical images.Results. The method was tested against simulated images of relativistic jets and applied to data from long-term monitoring of parsecscale radio jets in 3C 273 and 3C 120. Working at its coarsest resolution, WISE reproduces the previous results of a model-fitting evaluation of the structure and kinematics in these jets exceptionally well. Extending the WISE structure analysis to fine scales provides the first robust measurements of two-dimensional velocity fields in these jets and indicates that the velocity fields probably reflect the evolution of Kelvin-Helmholtz instabilities that develop in the flow.

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“…Random Sample Consensus (RANSAC) is a generally used strategy as it randomly selecting subsets and iteratively optimizing transform parameters. Paulson [8] and Song [9] applied RANSAC to removal outlier with a specific geometric transform. Wong [10] rectifies it as MDSAC to identify the maximum sum of squared distance between points within each subset.…”

Section: Introductionmentioning

confidence: 99%

A remote sensing images registration method based on compound outlier removal strategy

Fan

Liu

2011

MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications

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Registration is an essential preprocessing procedure for extensive application of remote sensing images. As a robust feature-based descriptor used for image registration, SIFT shows great superiority in describing graphical structure with its invariant characteristic to illumination, rotation and scale changes. However, it is not well performed under low contrast and weak edge conditions, which has a high appearance with remote sensing images. In this paper, a novel remote sensing image registration is proposed to cope with issues that SIFT hardly performs an acceptable accurate feature point matching. Frequency based and spatial based algorithms, which namely are Log Gabor wavelet transformation and discrete probability relaxation respectively, are introduced as a compound strategy to figure out mismatching candidates. Experiment results show that the proposed method could get lower root mean square error (RMSE) and higher correct matching ratio (CMR) than other typical methods utilized to compare with. As proposed compound outlier removal strategy is out of relying on transformation modal, it is considerably feasible and well performed without RANSAC algorithm. Furthermore, this method is invariant to shift, rotation, illumination and scale change dues to SIFT participation.

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Wavelet-based image registration

Cited by 15 publications

References 22 publications

Advanced Techniques for Scene Analysis

Advanced Techniques for Scene Analysis

Wavelet-based decomposition and analysis of structural patterns in astronomical images

A remote sensing images registration method based on compound outlier removal strategy

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