Image registration using log-polar mappings for recovery of large-scale similarity and projective transformations
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Citations
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An algorithm for computation of the scene geometry by the log-polar area matching around salient points
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References
Distinctive Image Features from Scale-Invariant Keypoints
Image registration methods: a survey
A survey of image registration techniques
Alignment by Maximization of Mutual Information
A pyramid approach to subpixel registration based on intensity
Related Papers (5)
Frequently Asked Questions (13)
Q2. What have the authors stated for future works in "Image registration using log-polar mappings for recovery of large-scale similarity and projective transformations" ?
Additional future work will accelerate correlation. It is worthwhile to examine whether this process may be accelerated by positioning the sliding window on areas of high information content only. Entropy, variance, or other statistically discriminating techniques can be used to quantify information content. Recent success with scale-invariant interest points ( e. g., SIFT ) suggest that the log-polar windows should be centered at these extracted positions.
Q3. What is the purpose of the log-polar transform?
The log-polar transform has two principal advantages: 1) rotation and scale invariance and 2) the spatially varying sampling in the retina is the solution to reduce the amount of information traversing the optical nerve while maintaining high resolution in the fovea and capturing a wide field of view.
Q4. What is the purpose of the INRIA method?
The INRIA method computes interest points at different scales, calculating at each scale a set of local descriptors that are invariant to rotation, translation, and illumination.
Q5. What measures have been used to test the similarity of a polar image?
The authors have extensively tested several similarity measures, including normalized correlation coefficient, phase correlation, and mutual information.
Q6. Why does the image in Fig. 3(b) defie recovery?
Although the Fourier–Mellin transform is able to correctly register the synthetic image shown in Fig. 3(c), the image in Fig. 3(b) defies recovery because of the lack of similarity in its spectra compared to that of the reference image.
Q7. What is the main contribution of this work?
An important contribution of this work is that the authors introduce a new method based on the log-polar transform in the spatial domain that works robustly with real images.
Q8. What is the new method for finding scale, rotation, and translation parameters?
The new method is based on multiresolution log-polar transformations to simultaneously find the best scale, rotation, and translation parameters.
Q9. How did the authors show the importance of the log-polar module?
In order to show the importance of the log-polar module, the authors ran the LMA without the estimated initial parameters from the log-polar module.
Q10. How many pairs of images are shown in Fig. 1?
The correlation coefficient values for the thirty pairs mentioned above are all above 0.9, which is very good considering camera noise and artifacts introduced by warping to produce the target images.
Q11. How does the LMA solve the system of equations?
The LMA solves the following system of equations in an iterative fashion:(5)where is the Hessian matrix and is the residual vector(6)(7)The authors can improve the standard Levenberg–Marquardt optimization algorithm outlined above by adding two modifications.
Q12. What was the first report of the authors?
They were, however, reported recently in [40] and [41], where the authors showed that rotation and scale introduce aliasing in the low frequencies.
Q13. Why does the Fourier–Mellin transform help register two images?
Note that artificial black backgrounds can help register two images because it ensures that the authors consider the same underlying content.