corner_shi_tomasi(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 the smaller eigenvalue of A:
((Axx + Ayy) - sqrt((Axx - Ayy)**2 + 4 * Axy**2)) / 2
Input image.
Standard deviation used for the Gaussian kernel, which is used as weighting function for the auto-correlation matrix.
Shi-Tomasi response image.
Compute Shi-Tomasi (Kanade-Tomasi) corner measure response image.
>>> from skimage.feature import corner_shi_tomasi, corner_peaksThis example is valid syntax, but we were not able to check execution
... 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]])
>>> corner_peaks(corner_shi_tomasi(square), min_distance=1,See :
... threshold_rel=0) array([[2, 2], [2, 7], [7, 2], [7, 7]])
The following pages refer to to this document either explicitly or contain code examples using this.
skimage.feature.corner.corner_shi_tomasi
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