Image Processing Reference

In-Depth Information

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Shi-Tomasi algorithm uses the eigenvalues of the Harris matrix. In this context, it difers

from Harris corner detector. The algorithm assumes that minimum of two eigenvalues of Har-

ris matrix determines the cornerness (
C
) of the point. Therefore, the corner decision is done

using the eigenvalues of the matrix. Shi-Tomasi algorithm gives more accurate results com-

pared with Harris detector. The algorithm is also more stable for tracking.

suicient information and excluding noise, Lucas-Kanade algorithm is successful. The al-

gorithm works for the corners obtained from Shi-Tomasi algorithm. Basically, the following

function should be minimized for each detected corner point as seen in differential ap-

proaches:

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With suitable
δx
and
δy
, optical flow vectors can be obtained. But, aperture problem is not

solved yet with this minimization. The solution approach for aperture problem is reflected to

the function definition
ε
(
δx
,
δy
)as follows:

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Summation on the
x
−
y
direction is a solution for the aperture problem. By using a window

w
centering the point (
x
,
y
), the estimation of optical flow of the point is extended with the

neighboring points.

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