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I found a paragraph in the book about SSD, can't get one thing:

Most commonly, the distance measure is the sum of squared differences. For two images f(x, y) and g (x, y) it is defined as
enter image description here

where the summation extends over the region of size (2n1 + 1) X (2n2 + 1)

I can not get, why does i changes from -n1 to n1, but not from 0 to n1. The similar about j. And why does summation goes over the (2n1 + 1) X (2n2 + 1) but not over the (n1)x(n2)

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(0, 0) represents the pixel in question. In order to go both positive and negative in both axes it is required to use both strictly positive (e.g. n<sub>1</sub>) and strictly negative (e.g. -n<sub>1</sub>) offsets. – Ignacio Vazquez-Abrams Jun 21 '12 at 19:06
But in Matrix, there are no negative offsets oO – user1448906 Jun 21 '12 at 19:17
As defined, SSD(d1,d2) appears to be an image itself for each d1 and d2: It depends on x and y. This might be correct, but I am unsure. – willem Jun 21 '12 at 19:26

migrated from stackoverflow.com Jun 22 '12 at 18:35

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