corner-shi-tomasi

corner_shi_tomasi

skimage.feature.corner_shi_tomasi(image, sigma=1) [source]

Compute Shi-Tomasi (Kanade-Tomasi) corner measure response image.

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

1 2	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:

1	((Axx + Ayy) - sqrt((Axx - Ayy)**2 + 4 * Axy**2)) / 2

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:	response : ndarray Shi-Tomasi response image.

References

[R140]

http://kiwi.cs.dal.ca/~dparks/CornerDetection/harris.htm

[R141]

http://en.wikipedia.org/wiki/Corner_detection

Examples

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>>> from skimage.feature import corner_shi_tomasi, 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]]) >>> corner_peaks(corner_shi_tomasi(square), min_distance=1) array([[2, 2],        [2, 7],        [7, 2],        [7, 7]])