Laplacian-of-Gaussian filter

The Laplacian-of-Gaussian (LoG) is a spatial filter used for edge enhancement and is closely related to the difference-of-Gaussians filter. The formulation of the LoG filter algorithm is based on the equation provided in the Hypermedia Image Processing Reference (HIPR) 2. The LoG operator calculates the second spatial derivative of an image. In areas where image intensity is constant, the LoG response will be zero. Near areas of change in intensity the LoG will be positive on the darker side, and negative on the lighter side. This means that at a sharp edge, or boundary, between two regions of uniform but different intensities, the LoG response will be:

• zero at a long distance from the edge,
• positive just to one side of the edge,
• negative just to the other side of the edge,
• zero at some point in between, on the edge itself.

The user may optionally choose to reflecting the data along image edges. NoData values in the input image are similarly valued in the output. The output raster is of the float data type and continuous data scale.

See Also:

• Difference-of-Gaussians Filter

Scripting:

The following is an example of a Python script that uses this tool:

wd = pluginHost.getWorkingDirectory()
inputFile = wd + "input.dep"
outputFile = wd + "output.dep"
stdDevDist = "0.75"
reflectEdges = "true"
args = [inputFile, outputFile, stdDevDist, reflectEdges]
pluginHost.runPlugin("FilterLoG", args, False)


This is a Groovy script also using the tool:

def wd = pluginHost.getWorkingDirectory()
def inputFile = wd + "input.dep"
def outputFile = wd + "output.dep"
def stdDevDist = "0.75"
def reflectEdges = "true"
String[] args = [inputFile, outputFile, stdDevDist, reflectEdges]
pluginHost.runPlugin("FilterLoG", args, false)


Credits:

• John Lindsay (2012)

References:

• Hypermedia Image Processing Reference (HIPR2)