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Li et al. [40] performed saliency detection by integrating the dense and sparse reconstruction errors of image regions.
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Measure Δ SNR compares squared error of image intensities.
A procrustes fit aligned by principal axes was performed to eliminate size differences before a Procrustes ANOVA was used to examine the error of image capture.
In addition, this correlation analysis provides an estimation of the local error of image alignment by measuring the displacement of the correlation peak within the correlation volume: this error was systematically below the cell size, corresponding to the error on cell position estimation (5-10 µm, i.e. the average diameter of a RG cell).
We concentrate on how to interactively reduce errors of deformable image registration if automatic registration methods cannot work well or registration errors are distinctive in clinical applications.
Since the modulus of this value increases with the similarity between two images, it is possible to use the difference between unity and the modulus of normalized cross correlation peak as an error metric of image registration.
The measurement errors of the images are analyzed.
To minimize measurement errors, majority of images were adjusted to be maximum intensity projection (MIP) images.
In general, the worry is that having an indeterminate signaling system serving as the mechanism for reputation tracking will, in effect, introduce errors in perception of image scores and destabilize cooperation as seen by Panchanathan and Boyd (2003).
Use the corresponding positions of the reconstructed images from the reference model to estimate the localization error of each image.
Generally, our estimation method is effective in that the average error rate of images tested under various intensities is less than 15% and the standard deviation is within 13%.
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