Connected Component Analysis

Identifies connected components in an image.

Connected component labeling (alternatively connected component analysis) is an algorithmic application of graph theory, where subsets of connected components are uniquely labeled. Connected component labeling is used in computer vision to detect connected regions in binary digital images.

A graph, containing vertices and connecting edges, is constructed from relevant input data. The vertices contain information required by the comparison heuristic, while the edges indicate connected 'neighbors'. An algorithm traverses the graph, labeling the vertices based on the connectivity and relative values of their neighbors. Connectivity is determined by the medium; image graphs, for example, can be 4-connected or 8-connected.

Copied from wikipedia [1] [2]

Options

Connection Type
Neighborhood definition.
  • FOUR_CONNECTED: These pixels are connected along one of the primary axes.
  • EIGHT_CONNECTED: These pixels are connected along either one, two, or all of the primary axes.
Background level
Background intensity value. Regions of this value are ignored. Normally the minimal value of the image type.
Dimensions
The dimension for which the connected component analysis should be applied individually. If, for instance, X and Y are selected, the CCA will be applied for each XY-plane individually and the connected components will have different ID's in the different planes.

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.

Input Ports

Icon
Images

Output Ports

Icon
Labeled segments

Popular Predecessors

Popular Successors

Views

Image Viewer
Another, possibly interactive, view on table cells. Displays the selected cells with their associated viewer if it exists. Available views are:
- XML
-- XML tree
- Missing Value Viewer
-- An empty viewer that is shown when the input cell has no value to display.
- 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.
- Image Viewer
-- This viewer renders the selected image-cell.
- Histogram Viewer
-- This viewer shows the histogram of the currently selected image.
- 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.
- Labeling View
-- View on a labeling/segmentation

Workflows

Links

Developers

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Installation

To use this node in KNIME, install the extension KNIME Image Processing from the below update site following our NodePit Product and Node Installation Guide:

v5.4

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

Plugin provider: University of Konstanz
Plugin version: 1.8.3.v202403231009
On NodePit since: 2024-12-06
Last update: 2024-12-17
Tags: Streamable
KNIME versions: v5.4, v5.3, v5.2, v5.1, v4.7, v4.6, v4.5, v4.4, v4.3, v4.2, v4.1, v4.0, v3.7, v3.6

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