structure_tensor
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skimage.feature.structure_tensor(image, sigma=1, mode='constant', cval=0)
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Compute structure tensor using sum of squared differences.
The structure tensor A is defined as:
A = [Axx Axy] [Axy Ayy]
which is approximated by the weighted sum of squared differences in a local window around each pixel in the image.
Parameters: image : ndarray
Input image.
sigma : float
Standard deviation used for the Gaussian kernel, which is used as a weighting function for the local summation of squared differences.
mode : {‘constant’, ‘reflect’, ‘wrap’, ‘nearest’, ‘mirror’}, optional
How to handle values outside the image borders.
cval : float, optional
Used in conjunction with mode ‘constant’, the value outside the image boundaries.
Returns: Axx : ndarray
Element of the structure tensor for each pixel in the input image.
Axy : ndarray
Element of the structure tensor for each pixel in the input image.
Ayy : ndarray
Element of the structure tensor for each pixel in the input image.
Examples
>>> from skimage.feature import structure_tensor >>> square = np.zeros((5, 5)) >>> square[2, 2] = 1 >>> Axx, Axy, Ayy = structure_tensor(square, sigma=0.1) >>> Axx array([[ 0., 0., 0., 0., 0.], [ 0., 1., 0., 1., 0.], [ 0., 4., 0., 4., 0.], [ 0., 1., 0., 1., 0.], [ 0., 0., 0., 0., 0.]])
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