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Different levels of blurring are simulated using the forward-scattered component of images taken at different distances from the scene: in (6) is increased varying from to meters to the scene at intervals.
We ran RMSE (root-mean-square error – measure of the differences between values predicted by a model or an estimator and the values actually observed) measurements for each levels of blurring, and for each measurement, we used 10,000 data items as input.
To yield visually comparable levels of blurring due to kernel bias we set the control parameter, q, to 0.02.
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All of the cameras allowed me to count the leaves on the potted plant at about 30 feet away, although with varying levels of blur and distortion.
Thus, they may have an advantage in high levels of blur.
In general, the two parameters depend on the levels of blur and noise in an observation (input image).
While the Morrison has had its fair share of camera time, everything we've seen has been rather unflattering angles with even more unflattering levels of blur.
In both the blur and the noise conditions, we see a significant difference between the two age groups only at intermediate levels of blur (or noise) and not at the extremes (i.e. the lowest and highest levels of blur or noise).
In the trials where we blurred the stimuli, this was accomplished by convolving full contrast text images with a Gaussian kernel of different sizes to achieve different levels of blur.
In our experiments, when we blurred our stimuli, we found a decreased threshold in older participants compared to younger participants, indicating that lower levels of blur had a deleterious effect on older participants' ability to perform the task.
In contrast to the work of Kline et al., we observed that levels of blur which only barely impacted the performance of younger participants had a profound impact on older participants' performance.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com