Tag Archives: Equation

Sum of Squared Differences

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The Sum of Squared Differences (SSD) is a way of determining the correlation between two image regions. It is usually involved when motion compensation needs to be done. SSD is defined as followed:

\sum\limits_{i,j\in W}(Image_1(i,j) - Image_2(x+i, y+j))^2

There are some variations of SSD. Like the Zero-mean Sum of Squared Differences (ZSSD):

\sum\limits_{i,j\in W}(I_1(i,j) - \overline{I}_1(i,j) - I_2(x+i, y+j) + \overline{I}_2(x+i, y+j) )^2

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Shi-Thomasi Corners

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Shi-Thomasi is a corner detection algorithm improved from Harris' corner detector. The improvement is the way how a certain region within the image is scored (and thus treated as corner or not). Where the Harris-corner detector determines the score R with the eigenvalues \lambda_1 and \lambda_2 of two regions (the second region is a shifted version of the first one and used to compare to the first one to see if the difference between the two is big enough to say if there is a corner or not) in the following way:

R=\det(\lambda_1\lambda_2)-k(\lambda_1+\lambda_2)^2

Shi and Thomas just use the minimum of both eigenvalues:

R=\min(\lambda_1,\lambda_2)

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