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Based on the least squares support vector machine (LS-SVM) geometric distortions correction, we propose a new image watermarking scheme in shiftable complex directional pyramid (PDTDFB) domain with good visual quality and reasonable resistance toward geometric distortions in this paper.
Functional scans for each participant were realigned to the first image, unwarped using field-map for distortions correction, and coregistered with the structural brain image.
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Several groups have shown that distortion correction can correct for non-linearities and reduce errors to well below 1 mm [18, 26, 27].
The correction of barrel-type distortions consists of two integral parts, which influence the appearance of the distortion-corrected images: the distortion correction method and the interpolation of missing information, once the distortion has been estimated correctly.
Prior to distortion correction, the data were motion corrected by image realignment with the reference scan.
Prior to distortion correction, the data was motion corrected by registration to the position of the reference scan.
Motion/eddy current distortion correction: Raw diffusion data were corrected for head motion and residual eddy current distortion by registering all images to the first T2 image, which was acquired with b = 0.
When comparing the distances for distorted and undistorted patches (dashed vs. solid lines) we notice that distortion correction leads to smaller feature distances for the majority of feature extraction methods.
As a consequence distortion correction usually consists of two steps: first, based on one or more distorted images, the camera parameters (intrinsic and extrinsic, including the CoD) and the parameters for the assumed radial distortion model are estimated.
A geometric-distortion correction method is proposed.
Table 3 Performances of the distortion correction.
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