![]() The localization algorithm plays an important role in obtaining a high quality of localization nanoscopy images. Third, a localization algorithm estimates the emitter locations from the data movie and produces a localization nanoscopy image of the specimen ultrastructure. Repeating this process, a data movie that consists of a large number of data frames is acquired. Second, in each frame time a random subset of emitters are activated by a laser and emit photons that pass through an optical lens and produce a data frame acquired by a camera. First, a set of emitters are attached to ultrastructure of a specimen. In stochastic optical localization nanoscopy-PALM 1, STORM 2, FPALM 3 and (d)STORM 4, a localization nanoscopy image is produced by three steps. The results suggest development of two kinds of localization algorithms: the algorithms that can exploit the temporal correlation of FFL images and the unbiased localization algorithms. ![]() The ideas about how to develop an algorithm to exploit the temporal correlation of FFL images are also briefly discussed. Numerical examples are taken and the results confirm the prediction of analysis. Analyzed and revealed are also several statistical properties of RMSMD and RMSE and their relationship with respect to a large number of data frames, bias and variance of localization errors, small localization errors, sample drift, and the worst FFL image. It is shown that RMSMD and RMSE can be potentially reduced by a maximum fold equal to the square root of the average number of activations per emitter. In this paper, we analyze the properties of the FFL images in terms of root mean square minimum distance (RMSMD) and root mean square error (RMSE). The temporal correlation contained in the FFL images, if exploited, can improve the localization accuracy and the image quality. The majority of localization algorithms in the literature estimate emitter locations by frame-by-frame localization (FFL), which exploit only the spatial correlation and leave the temporal correlation into the FFL nanoscopy images. These are my two cents as a non-animator (or an artist of any kind).Įdit: Most of the artwork in drawn at a much higher resolution than the final result, in other words, artists have a lot of room for "error" when they work on the finer details.A data movie of stochastic optical localization nanoscopy contains spatial and temporal correlations, both providing information of emitter locations. I do know that they draw the main keyframes and then fill in the frames between those, and animation can be quite forgiving when it comes to linework, as the average person can't (unless they slow down the video) notice the "messy" lineart. So I wonder if there are some nuances or techniques to reducing the boiling, other than developing machine-like precision?Īs far as I know, animations are not pixel perfect, just look for any post and you will notice that in every frame the lines "giggle" a bit, perfectly straight lines make the animation look somewhat motionless, and if I am not mistaken, they simply "trace" the lines from the previous frame or they copy it, but I am not sure. I'm predicting that the answers will be "just practice more" (It does begin to feel like it's a "have you tried to turn it off and on again?" version of the art community), but at the same time, you can spend years practicing drawing perfectly straight lines.
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