Image analysis of GFP-tagged protein localization bursts
Yep, the Hough Transform is a way to pick out shapes you're interested in, in this case they probably have it set to find circles, and they use that to segment the image.

I think that you have interpreted their methods correctly.  For each cell they make a trace of localization score vs. time, localization score defined in arbitrary units as the difference between the mean of the five brightest pixels and the mean of the remaining pixels in the cell.  I think the lowest 20% refers to the frames in the video that have the lowest 20% of localization scores.  They take the lowest 20% of localization scores, calculate a standard deviation, and then for any frame that has a localization score that is more than 1 standard deviation above the mean of those 20%, you say that frame exhibits a burst of localization.  If I'm understanding this correctly, this process would be repeated on every individual cell.

My interpretation is that the 20% doesn't have anything to do with the signal intensity from the background pixels, and it comes from analyzing the series over time, not a single image.