So this here is a bit more of a theoretical/fundamental question; what is the influence (if any) of noise on polimago training and the quality of the subsequent training?
Specifically, I want to increase the camera gain for a low light application, but since it can be a lot of work to thoroughly test the possible differences between the two situations (dark not noisy image vs brighter noisy image), perhaps some general predictions can be made?
for instance, I can imagine that the noise from the higher gain is more of an issue at smaller training sets. Is this the case?
It depends on the parameterization to some extend. If you make the classifier more sensitive to fine details, for instance with many “a” in your pre-processing code, then yes, noise can be an issue. If you want to detach the classifier from noise, use one or two “p” in your pre-processing code to smooth the image a bit.
In general, I’ve not seen noise to be a noteworthy issue with Polimago, judging solely based on my personal experience. That said, the images should probably not be of abysmal quality, in regard to noise.
Well, the gain isn’t that high, but there is an increase in noise. But the tradeoff was motion blur, noise or dark image. So I was kind of trying to find out which would hurt the classifier the most. Thanks!
Motion blur might create a bigger issue than the noise I think. I’d trade some noise for less motion blur. With noise, the overall appearance of a given object might only be changed slightly, but motion blur can change it more drastically and is less deterministic (not to say that noise is deterministic ).
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