corner_foerstner(image, sigma=1)

This corner detector uses information from the auto-correlation matrix A:

A = [(imx**2)   (imx*imy)] = [Axx Axy]
    [(imx*imy)   (imy**2)]   [Axy Ayy]

Where imx and imy are first derivatives, averaged with a gaussian filter. The corner measure is then defined as:

w = det(A) / trace(A)           (size of error ellipse)
q = 4 * det(A) / trace(A)**2    (roundness of error ellipse)

Parameters

image : ndarray

Input image.

sigma : float, optional

Standard deviation used for the Gaussian kernel, which is used as weighting function for the auto-correlation matrix.

Returns

w : ndarray

Error ellipse sizes.

q : ndarray

Roundness of error ellipse.

Compute Foerstner corner measure response image.

Examples

This example is valid syntax, but we were not able to check execution

>>> from skimage.feature import corner_foerstner, corner_peaks
... square = np.zeros([10, 10])
... square[2:8, 2:8] = 1
... square.astype(int)
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
       [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
       [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
       [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
       [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
       [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])

This example is valid syntax, but we were not able to check execution

>>> w, q = corner_foerstner(square)
... accuracy_thresh = 0.5
... roundness_thresh = 0.3
... foerstner = (q > roundness_thresh) * (w > accuracy_thresh) * w
... corner_peaks(foerstner, min_distance=1, threshold_rel=0)
array([[2, 2],
       [2, 7],
       [7, 2],
       [7, 7]])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

skimage.feature.corner.corner_foerstner

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