gabor-kernel

gabor_kernel

skimage.filters.gabor_kernel(frequency, theta=0, bandwidth=1, sigma_x=None, sigma_y=None, n_stds=3, offset=0) [source]

Return complex 2D Gabor filter kernel.

Gabor kernel is a Gaussian kernel modulated by a complex harmonic function. Harmonic function consists of an imaginary sine function and a real cosine function. Spatial frequency is inversely proportional to the wavelength of the harmonic and to the standard deviation of a Gaussian kernel. The bandwidth is also inversely proportional to the standard deviation.

Parameters:

frequency : float Spatial frequency of the harmonic function. Specified in pixels. theta : float, optional Orientation in radians. If 0, the harmonic is in the x-direction. bandwidth : float, optional The bandwidth captured by the filter. For fixed bandwidth, sigma_x and sigma_y will decrease with increasing frequency. This value is ignored if sigma_x and sigma_y are set by the user. sigma_x, sigma_y : float, optional Standard deviation in x- and y-directions. These directions apply to the kernel before rotation. If theta = pi/2, then the kernel is rotated 90 degrees so that sigma_x controls the vertical direction. n_stds : scalar, optional The linear size of the kernel is n_stds (3 by default) standard deviations offset : float, optional Phase offset of harmonic function in radians.

Returns:

g : complex array Complex filter kernel.

References

[R190]

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

[R191]

http://mplab.ucsd.edu/tutorials/gabor.pdf

Examples

>>> from skimage.filters import gabor_kernel
>>> from skimage import io
>>> from matplotlib import pyplot as plt  

>>> gk = gabor_kernel(frequency=0.2)
>>> plt.figure()        
>>> io.imshow(gk.real)  
>>> io.show()           

>>> # more ripples (equivalent to increasing the size of the
>>> # Gaussian spread)
>>> gk = gabor_kernel(frequency=0.2, bandwidth=0.1)
>>> plt.figure()        
>>> io.imshow(gk.real)  
>>> io.show()