Calculates eigenvalues and eigenvectors of image blocks for corner detection

dst = cv.cornerEigenValsAndVecs(src)
dst = cv.cornerEigenValsAndVecs(src, 'OptionName', optionValue, ...)

Input

• src Input single-channel 8-bit or floating-point image.

Output

• dst Image to store the results. It has the same row/column size as src and the single type (where size(dst,3)==6). Eigenvalues and eigenvectors are stored along the channels, see below.

Options

• BlockSize Neighborhood size (see details below). default 5.
• KSize Aperture parameter for the cv.Sobel operator. default 3.
• BorderType Pixel extrapolation method. default 'Default'

For every pixel p, the function cv.cornerEigenValsAndVecs considers a blockSize x blockSize neigborhood S(p). It calculates the covariation matrix of derivatives over the neighborhood as:

M = [
    \sum_{S(p)}(dI/dx)^2        \sum_{S(p)}(dI/dx * dI/dy)
    \sum_{S(p)}(dI/dx * dI/dy)  \sum_{S(p)}(dI/dy)^2
]

where the derivatives are computed using the Sobel operator.

After that, it finds eigenvectors and eigenvalues of M and stores them in the destination image as (lambda_1, lambda_2, x_1, y_1, x_2, y_2) where:

• lambda_1, lambda_2 are the non-sorted eigenvalues of M
• x_1, y_1 are the eigenvectors corresponding to lambda_1
• x_2, y_2 are the eigenvectors corresponding to lambda_2

The output of the function can be used for robust edge or corner detection.