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Template matching

[![Python 3.10+](https://img.shields.io/badge/python-3.10+-3776AB?logo=python&logoColor=white)](https://www.python.org/)

Result

Sr	Scene	SSD	SSD normalised	Cross-correlation	NCC
1	hippo in green water · entropy 5.3	225	342	225	99
2	elephants and grooms · entropy 7.4	0	111	111	305
3	two at a railing · entropy 7.6	239	239	239	0
4	monks at a noticeboard · entropy 7.9	154	144	144	0

The finding

The invariances, and the sign that reveals them

**At a tenth of the brightness NCC still localises every template and SSD normalised localises none.** Dividing by the standard deviation is exactly a scale invariance, and the sweep has to reach 10× before the claim is visible at all — at the usual gentle 0.9 every method still works.

> ZNCC holds 1.000 at every negative offset; NCC falls to 0.458. Subtracting > the mean is exactly an offset invariance, and NCC does not subtract the mean. > > It takes a negative offset to show it. Brightening clips at white and > destroys the structure for everyone — at +0.4 NCC and ZNCC both sit at 0.958 > and the difference between them is invisible. Darkening clips only in the > shadows, which leaves enough for a mean-subtracting method and not enough for > anything else. A sweep over positive offsets alone would have reported that > NCC and ZNCC are equivalent.

The algebra says the same thing with no image search involved. ZNCC of a patch against a·patch + b is 1.0 for any positive a and any b:

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Figures

Elsewhere

Code and full results on GitHub (code private)

Every number is read from the file the run produced. github.com/hammasbuilds · kaggle.com/muhammadhammas13