Analytical approximations of translational subpixel shifts in both signal and image registrations are derived by setting the derivatives of a normalized cross correlation function to zero and solving them. Without the...
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ISBN:
(纸本)9780819472946
Analytical approximations of translational subpixel shifts in both signal and image registrations are derived by setting the derivatives of a normalized cross correlation function to zero and solving them. Without the need of iterative searching, this methods achieves a complexity of only O(mn), given an image size of m x n. Without the need to upsample, computation memory is also saved. Tests using simulated signals and images show good results.
Some image processing applications require an image meet a quality metric before processing it. If an image is so degraded that it is difficult or impossible to reconstruct, the input image may be discarded. In this p...
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ISBN:
(纸本)9780819472946
Some image processing applications require an image meet a quality metric before processing it. If an image is so degraded that it is difficult or impossible to reconstruct, the input image may be discarded. In this paper, we present a metric that measures the relative sharpness with respect to a reference image frame. The reference frame may be a previous input image or an output frame from the system. The sharpness metric is based on analyzing edges. The assumption of this problem is that input images are similar to each other in terms of observation angle and time.
The quantification of synchrony is important for the study of large-scale interactions in the brain. Current synchrony measures depend on the energy of the signals rather than the phase, and cannot be reliably used as...
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ISBN:
(纸本)9780819472946
The quantification of synchrony is important for the study of large-scale interactions in the brain. Current synchrony measures depend on the energy of the signals rather than the phase, and cannot be reliably used as measures of neural synchrony. Moreover, the current methods are insufficient since they are limited to pairs of signals. These approaches cannot quantify the synchrony across a group of electrodes and over time-varying frequency regions. In this paper, we propose two new measures for quantifying the synchrony between both pairs and groups of electrodes using time-frequency analysis. The proposed measures are applied to electroencephalogram (EEG) data to quantify neural synchrony.
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