Purpose

The module Sobel3D computes gradients of a grey-value image by applying a Sobel kernel for each main axis direction.

The module can also compute the gradients strengths and a gradient vector image.

Windows

Default Panel

../../../Modules/ML/MLKernel/mhelp/Images/Screenshots/Sobel3D._default.png

Input Fields

input0

name: input0, type: Image

Output Fields

output0

name: output0, type: Image

Depending on the mode (Gradient Strength, Edge Detection or Estimation) the output is a grey-scale image, a binary edge-map or a RGB-image, where the rgb-values represent xyz-vector components.

Parameter Fields

Field Index

autoCalcMinMax: Bool Min. Threshold: Float useMinMax: Bool
Border Handling: Enum Non Edge Value: Float  
Edge Value: Float outputMax: Double  
Fill Value: Double outputMin: Double  
Filter Mode: Enum referenceExtentMode: Enum  
Max: Double Scale with voxel size: Bool  
Max. Threshold: Float setAutoMinMax: Trigger  
Min: Double Use: Bool  

Visible Fields

Border Handling

name: borderHandling, type: Enum, default: PadSrcClamp, deprecated name: edgeMode

Defines the border handling mode.

See Border Handling in Kernel Operations for details.

Values:

Title Name
No Pad NoPad
Pad Src Fill PadSrcFill
Pad Dst Fill PadDstFill
Pad Dst Fill With Orig PadDstFillWithOrig
Pad Src Undefined PadSrcUndefined
Pad Dst Undefined PadDstUndefined
Pad Src Clamp PadSrcClamp

Fill Value

name: fillValue, type: Double, default: 0

Sets the fill value for certain Border Handling modes.

Min

name: intervalMinOfFilteredVoxels, type: Double, default: 30, deprecated name: imageIntervalMin

Sets the minimum value threshold for outputting a subset of voxel.

Max

name: intervalMaxOfFilteredVoxels, type: Double, default: 50, deprecated name: imageIntervalMax

Sets the maximum value threshold for outputting a subset of voxel.

Use

name: useIntervalOfFilteredVoxels, type: Bool, default: FALSE

If checked, the module outputs only on a subset of voxels defined by a value range.

Min. Threshold

name: edgeThresholdMinimum, type: Float, default: 31.25, minimum: 0, maximum: 150000, deprecated name: edgeThresholdMinimumFld

Set the minimum value of one kernel convolution result for thresholding in Edge Detection or in Estimation mode.

Max. Threshold

name: edgeThresholdMaximum, type: Float, default: 46.875, minimum: 0, maximum: 150000, deprecated name: edgeThresholdMaximumFld

Set the maximum value of one kernel convolution result for thresholding in Edge Detection or in Estimation mode.

Edge Value

name: detectedEdgeValue, type: Float, default: 32, minimum: 0, maximum: 4096, deprecated name: detectedEdgeValueFld

If in Edge Detection mode, this value will be set into the output image when the convolution value lies within the threshold range.

Non Edge Value

name: detectedNonEdgeValue, type: Float, default: 0, minimum: 0, maximum: 4096, deprecated name: detectedNonEdgeValueFld

If in Edge Detection mode, this value will be set into the output image when the convolution value lies outside of the threshold value.

Scale with voxel size

name: useVoxelSize, type: Bool, default: TRUE

If checked and in Gradient Strength mode, each resulting convolution value is divided by the voxel size in the particular (xyz) direction.

Filter Mode

name: filterMode, type: Enum, default: Gradient Strength, deprecated name: FilterMode

Sets the filter mode of this module.

Values:

Title Name Description
Gradient Strength Gradient Strength The resulting sum of the kernel convolutions is written into the ouput image, which is a grey-value image representing the strengths of the gradients in each voxel.
Edge Detection Edge Detection Thresholds the convolution values in each direction: if any of these values is within the threshold range, an Edge Value is written into the output image, a Non Edge Value is written else, resulting in a binary edge image.
Estimation Estimation The values of the three convolutions are used as vector components, where the x,y,z values are coded into r,g,b values of the output image, resulting in a RGB-image.
Gradient Estimation And Strength Packed GradientEstimationAndStrengthPacked

Estimates the gradient and its strength. The gradient and strength are normalized and packed to the range 0-1 using:

rgb = (grad / gradStrength + 1.) * 0.5
a = gradStrength / maxGradStrength

This is the format that makes it usable for the GVR and SoPathTracer, since they will convert this input gradient image to RGBA8 and have the same packing convention.

Hidden Fields

referenceExtentMode

name: referenceExtentMode, type: Enum, default: Overlap

Values:

Title Name
Overlap Overlap
Input0 Ext Without Fill Input0_ExtWithoutFill
Input0 Ext With Fill Input0_ExtWithFill
Max Extents Without Fill MaxExtentsWithoutFill
Max Extents With Fill MaxExtentsWithFill

autoCalcMinMax

name: autoCalcMinMax, type: Bool, default: TRUE

setAutoMinMax

name: setAutoMinMax, type: Trigger

outputMin

name: outputMin, type: Double, default: 1

outputMax

name: outputMax, type: Double, default: 0

useMinMax

name: useMinMax, type: Bool, default: FALSE