structure-tensor

structure_tensor

skimage.feature.structure_tensor(image, sigma=1, mode='constant', cval=0) [source]

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.]])