Computer Vision Foundations · free preview

Correlation and Template Matching

Cross-correlation slides a template without flipping it, and normalization rescues matching from brightness traps.

1. Correlation is convolution without the flip

Correlation is convolution without the flip

2. Finding a letter with a template

Finding a letter with a template

3. Normalizing the score

Normalizing the score

4. What matching survives

What matching survives

5. The flip is the whole difference

A kernel shaped like a rising ramp, small values on the left and large on the right, is applied to an image once by convolution and once by cross-correlation. How do the outputs differ?

6. Why the raw score betrays the letter

Raw cross-correlation is used to find a dark letter template on a bright scanned page, and the response map peaks strongly on a plain bright margin. What went wrong?

7. Subtracting the template mean

A template has pixel values (10,10,10,10)(10, 10, 10, 10). After subtracting the template's mean, write the value of the first pixel.

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