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Convolver

StreamableKNIME Image Types and Nodes version 1.8.3.202012050717 by University of Konstanz

Generic Convolver to convolve an image with a given kernel (i.e. another image). One way to create kernels is the "Convolution Kernel Creator"-node. Remark: All incoming kernels must have the same number of dimensions, else you have to use two different convolvers (one after the other).

Options

Kernel Settings
The column containing the kernel images.
Dimension Selection
Select the orientation of the kernel. The number of select dimensions must be the same as the number of dimensions of the kernel. If your kernel has the same dimensionality as your input image, this setting has no effect).
Calculate as Float
If checked, the complete convolution will be calculated in float precision. The result will also be a float image.
Out of Bounds Selection

The 'OutOfBounds Strategy' is used when an algorithm needs access to pixels which lie outside of an image (for example convolutions). The strategy determines how an image is extended, for examples see here

Column Selection

Column Creation Mode

Mode how to handle the selected column. The processed column can be added to a new table, appended to the end of the table, or the old column can be replaced by the new result

Column Suffix
A suffix appended to the column name. If "Append" is not selected, it can be left empty.
Column Selection
Selection of the columns to be processed.

Convolution Settings

ImgLib 2 Add dimension Convolution

Based on ImgLib2's Fourier Transformation implemenation. Image and Kernels are converted to FourierSpace and convolved there. Each convolution is applied on the input image individually. The results are stored in an additional dimension.

ImgLib 2 Iterative Convolution

Based on ImgLib2's Fourier Transformation implemenation. Image and Kernels are converted to FourierSpace and convolved there. Each convolution is applied on the result of the last one

Direct Iterative Convolution

Direct convolution of the image. Convolution takes place in image space. Each convolution is applied on the result of the last one

Direct Convolution (Add Dimension)

Direct convolution of the image. Convolution takes place in image space. Each convolution is applied on the input image individually. The results are stored in an additional dimension.

Input Ports

Icon
Images
Icon
Kernels

Output Ports

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Convolved Images

Views

Image Viewer
Another, possibly interactive, view on table cells. Displays the selected cells with their associated viewer if it exists. Available views are:
- BigDataViewer
-- A viewer shown when the user selects an interval of rows and columns in the viewer. This viewer combines all images and labelings in the selected interval to one image by rendering them next to each other. Alternatively, the images and labelings can be layed over each other.
- XML
-- XML tree
- Histogram Viewer
-- This viewer shows the histogram of the currently selected image.
- Missing Value Viewer
-- An empty viewer that is shown when the input cell has no value to display.
- Labeling View
-- View on a labeling/segmentation
- Combined View
-- A viewer shown when the user selects an interval of rows and columns in the viewer. This viewer combines all images and labelings in the selected interval to one image by rendering them next to each other. Alternatively, the images and labelings can be layed over each other.
- Image Viewer
-- This viewer renders the selected image-cell.

Best Friends (Incoming)

Best Friends (Outgoing)

Workflows

Installation

To use this node in KNIME, install KNIME Image Processing from the following update site:

KNIME 4.3

A zipped version of the software site can be downloaded here.

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Developers

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