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There are three major advantages of our approach: (1) it solves the problem of manual target selection as done by the researchers in morphing; (2) automatic target detection achieves smooth transition from source to destination image; and (3) control points are automatically detected for morphing because the algorithm detects the target based on the matching features.
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The algorithm detects these transitions by analyzing the probability function.
The algorithm detects positively stained pixels within the image.
The algorithm detects orthologous relations between two (groups of) species.
After the algorithm detects the IS/OS boundary, the BM/Choroid boundary is detected.
The algorithm detected 160 complexes in total.
The algorithms detected all documented clusters.
Using the data available as of June 1, 2007, we considered the number of reported outbreaks (a minimum of 4 cases because the algorithm cannot detect outbreaks with <4 cases), from week 5 of 2005 through week 4 of 2007.
This indicates the proposed algorithm detects denser sub-network.
And here is where it gets spooky: because the algorithm has to detect the reflection in a window, for example, it can also create a clear picture of the reflection (or the fence — but most fences I have ever seen aren't very interesting…).
Then, the app's image processing algorithm detects and clusters the works, knitting in location data from Four Square's API and content from Instagram's API.
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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