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If your templates are all based on some kind of text you may use some kind of OCR to match the text itself and not only by features. Regarding features, you may read: A Comparative Analysis of SIFT, SURF, KAZE, AKAZE, ORB, and BRISK. Specifically have aloo at the sections: It seems your feature extractor usually use corners while you need more general ...


3

In case you can shoot a video of the static scene than a blinking light would be the easiest as you could easily detect it by subtracting the n - 1 frame from the n frame until you see something with high values. If you take a still shot you can use 2 main ideas: If the colors of the scene are from a given plate, find a color very different in Hue and make ...


1

I believe Haar Cascades(used by Viola-Jones) are inherently scale-invariant. Also severely deprecated by modern Neural Networks, but I know nothing about those. It also doesn't do any OCR - if you need that you would need to run a separate algorithm on the extracted sub-image.


1

Let's label your corners like this: 1-------2 | | 3-------4 For your destination. Let, $P_1, P_2, P_3, \text{ and } P_4 $ be vectors representing the corners in pixel coordinates. For your source, Let $H$ and $W$ represent the height and width, and $(x,y)$ be a point in that rectangle you want to project. You can calculate your coefficients like ...


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