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gauss images.imfilter



gauss -- convolve a list of images with an elliptical Gaussian function


gauss input output sigma


List of images to be convolved with the elliptical Gaussian function.
List of output images. The number of output images must equal the number of input images. If the input image name equals the output image name, the convolved image will replace the input image.
The sigma of the Gaussian function in pixels along the direction theta of the major axis of the Gaussian function.
ratio = 1.
The ratio of the sigma in the minor axis direction to the sigma in the major axis direction of the Gaussian function. If ratio is 1 the Gaussian function is circular.
theta = 0.
The position of the major axis of the elliptical Gaussian function. Theta is measured counter-clockwise from the x axis and must be between 0 and 180 degrees.
nsigma = 4.0
The distance along the major axis of the Gaussian function at which the kernel is truncated in sigma pixels.
bilinear = yes
Use the fact that the Gaussian function is separable (bilinear) in x and y if theta = 0, 90, or 180, to compute the 2D convolution more efficiently? Bilinear is always set to "no" internally, if the position angle of the major axis of the Gaussian is other than 0, 90 or 180 degrees.
boundary = "nearest"
The algorithm used to compute the values of the out of bounds pixels. The options are:
Use the value of the nearest boundary pixel.
Use a constant value.
Generate a value by reflecting around the boundary.
Generate a value by wrapping around to the opposite side of the image.
constant = 0.
The constant for constant-valued boundary extension.


GAUSS convolves the list of images in input with the Gaussian kernel specified by sigma , ratio , theta and nsigma and places the convolved images in output . If the image names in input equal the image names in output the convolution is performed in place and the original images are overwritten. Out of bounds pixels are computed using the algorithm specified by boundary .

If bilinear is "yes" and the major axis of the Gaussian kernel is aligned along either the x or y axis, GAUSS uses the fact that the Gaussian function is mathematically separable (bilinear) in x and y to speed up the convolution process. A bilinear 2D convolution kernel in x and y is one which can be separated into two equivalent 1D convolution kernels in x and y respectively.

Although the bilinear approximation and the full 2D convolution are mathematically equivalent, the user will actually see SMALL differences between an image convolved with the full 2D kernel and the same image convolved with the equivalent bilinear kernel. These differences are the result of the finite size of the convolution kernel (the integration does not extend to infinity in either direction), and the fact that off-axis kernel elements outside the nsigma limit cannot be set to 0 in the bilinear case as they are in the full 2D case. Therefore the bilinear kernel is less radially symmetric than the full 2D kernel. In most cases the differences are small and more than made up for by the greatly decreased execution time.

The Gaussian kernel has an elliptical cross-section and Gaussian profile and is defined mathematically as follows.

1. Circularly Symmetric Gaussian Function

    ratio = 1   theta = 0.0   N = normalization factor

    G = N * exp (-0.5 * (r / sigma) ** 2)

2. Elliptical Gaussian Function (Theta = 0, 90 or 180)

    sigmax = sigma   sigmay = ratio * sigmax   N = normalization factor

    A = cos (theta) ** 2 / sigmax ** 2 + sin (theta) ** 2 / sigmay ** 2

    B = 0.0

    C = sin (theta) ** 2 / sigmax ** 2 + cos (theta) ** 2 / sigmay ** 2

    z = A * x ** 2 + B * x * y + C * y ** 2 

    G = N * exp (-0.5 * z)

3. Elliptical Gaussian  Function (Arbitrary Theta)

    sigmax = sigma   sigmay = ratio * sigmax   N=normalization factor

    A = cos (theta) ** 2 / sigmax ** 2 + sin (theta) ** 2 / sigmay ** 2

    B = 2 * (1 / sigmax ** 2 - 1 / sigmay ** 2) * sin (theta) * cos (theta)

    C = sin (theta) ** 2 / sigmax ** 2 + cos (theta) ** 2 / sigmay ** 2

    z = A * x ** 2 + B * x * y + C * y ** 2 

    G = N * exp (-0.5 * z)


1. Convolve an image with a circular Gaussian function of sigma 2.0, and size 4.0 sigma using nearest neighbour boundary extension and the bilinear kernel.

cl> gauss m83 m83.gau 2.0

2. Do the same convolution using the full 2D kernel.

cl> gauss m83 m83.gau.2D 2.0 bilinear-

3. Convolve an image with an elliptical Gaussian function whose sigma in the major and minor axis direction is 2.0 and 1.5 respectively, and whose position angle is 45 degrees, using wrap around boundary extension. In this case the full 2D kernel is used by default.

cl> gauss m84 m84.gau 2.0 ratio=.75 theta=45. bound=wrap


GAUSS requires approximately 30 and 8 cpu seconds to convolve a 512 square real image with circularly symmetric Gaussian function of sigma 2 pixels, using the full 2D kernel and the bilinear kernel respectively, on a Sparc Station 1.



convolve, gradient, laplace, boxcar

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