diff_of_gauss
— Approximate the LoG operator (Laplace of Gaussian).
diff_of_gauss(Image : DiffOfGauss : Sigma, SigFactor : )
diff_of_gauss
approximates the Laplace-of-Gauss
operator by a difference of Gaussians. The standard deviations of
these Gaussians can be calculated, according to Marr, from the
Parameter Sigma
of the LoG and the ratio of the two
standard deviations (SigFactor
) as:
For a , according
to Marr, an approximation to the Mexican-Hat-Operator results. The
resulting image is stored in DiffOfGauss
.
Note that filter operators may return unexpected results if an image with a reduced domain is used as input. Please refer to the chapter Filters.
Image
(input_object) (multichannel-)image(-array) →
object (byte / uint2)
Input image
DiffOfGauss
(output_object) (multichannel-)image(-array) →
object (int2)
LoG image.
Sigma
(input_control) real →
(real)
Smoothing parameter of the Laplace operator to approximate.
Default: 3.0
Suggested values: 2.0, 3.0, 4.0, 5.0
Minimum increment: 0.01
Recommended increment: 0.1
Restriction:
Sigma > 0.0
SigFactor
(input_control) real →
(real)
Ratio of the standard deviations used (Marr recommends 1.6).
Default: 1.6
Minimum increment: 0.01
Recommended increment: 0.1
Restriction:
SigFactor > 0.0
read_image(Image,'fabrik') diff_of_gauss(Image,Laplace,2.0,1.6) zero_crossing(Laplace,ZeroCrossings)
The execution time depends linearly on the number of pixels and the size of sigma.
diff_of_gauss
returns 2 (
H_MSG_TRUE)
if all parameters are
correct. If the input is empty the behavior can be set via
set_system('no_object_result',<Result>)
. If
necessary, an exception is raised.
D. Marr: “Vision (A computational investigation into human representation and processing of visual information)”; New York, W.H. Freeman and Company; 1982.
Foundation