Module: Gradient

Description:

Derives the image and extracts the norm of the gradient. The derivation is done either by convolution of a gradient kernel, which can be either the Sobel or the Prewitt kernel, or by finding the tangent to a cubic spline interpolation of the image.

Gradient of an image.

Connections:

Image

[required]
Input image, of type HxUniformScalarField3..

Ports:

Type

Specifies the gradient method to use, i.e. either convolution by a Sobel or Prewitt kernel, spline interpolation, or Logarithmic Image Processing (LIP) contrast estimation¹. For this last one, LIP subtractions are performed on pixel greylevels before and after the current position, in each direction (X, Y, and Z).

Range

If the LIP framework is used, the range of greyvalues must be specified, with minimum analogous to total transparency (in the transmittance framework, no object obstructs the light) and maximum complete opaqueness.

Norm

Choice of the norm, 2 is the Euclidean.

Derive

Push the first button to derive the input. Push the second button to derive the output.

Commands:

Additional options can be accessed when typing in the console Gradient COMMAND_NAME.

`verbose`

Displays timing information after the computation. Retype to hide info.

`create`

Runs the computation. Returns the name of the output, so it can be used in a script, such as set RESULT [Gradient create].

References:

¹ Jourlin M., Pinoli, J. C. (2001). Logarithmic Image Processing: The mathematical and physical framework for the representation and processing of transmitted images. Advances in imaging and electron physics 115, p. 129-196.