hessian_matrix(image, sigma=1, mode='constant', cval=0, order='rc')
The Hessian matrix is defined as:
H = [Hrr Hrc]
[Hrc Hcc]
which is computed by convolving the image with the second derivatives of the Gaussian kernel in the respective r- and c-directions.
Input image.
Standard deviation used for the Gaussian kernel, which is used as weighting function for the auto-correlation matrix.
How to handle values outside the image borders.
Used in conjunction with mode 'constant', the value outside the image boundaries.
This parameter allows for the use of reverse or forward order of the image axes in gradient computation. 'rc' indicates the use of the first axis initially (Hrr, Hrc, Hcc), whilst 'xy' indicates the usage of the last axis initially (Hxx, Hxy, Hyy)
Element of the Hessian matrix for each pixel in the input image.
Element of the Hessian matrix for each pixel in the input image.
Element of the Hessian matrix for each pixel in the input image.
Compute Hessian matrix.
>>> from skimage.feature import hessian_matrixSee :
... square = np.zeros((5, 5))
... square[2, 2] = 4
... Hrr, Hrc, Hcc = hessian_matrix(square, sigma=0.1, order='rc')
... Hrc array([[ 0., 0., 0., 0., 0.], [ 0., 1., 0., -1., 0.], [ 0., 0., 0., 0., 0.], [ 0., -1., 0., 1., 0.], [ 0., 0., 0., 0., 0.]])
The following pages refer to to this document either explicitly or contain code examples using this.
skimage.feature.corner.hessian_matrix
skimage.feature.corner._hessian_matrix_image
skimage.feature.corner.hessian_matrix_eigvals
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