GraphCut 2D

From wikipedia: Graph partitioning methods can effectively be used for image segmentation. In these methods, the image is modeled as a weighted, undirected graph. Usually a pixel or a group of pixels are associated with nodes and edge weights define the (dis)similarity between the neighborhood pixels. The graph (image) is then partitioned according to a criterion designed to model "good" clusters. Each partition of the nodes (pixels) output from these algorithms are considered an object segment in the image. see also (Graph Cuts and Efficient N-D Image Segmentation (Boykov et. al)): http://www.springerlink.com/content/j3k24j8347k42425/fulltext.pdf

Options

lambda

Value weighting the local (high lambda) / global (low lambda) influence of seeding points

Feature Dimension selection (optional)

The selected dimension is regarded as a feature vector. e.g.: if you select in the dim selection X and Y in a RGB image and in the feature dim selection C, the graph weights are calculated according to the color vectors (R,G,B) of the RGB image

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

Images and Seed Labeling (optional)

Output Ports

Image BitType

Views

Image Viewer

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

Installation

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

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

You don't know what to do with this link? Read our NodePit Product and Node Installation Guide that explains you in detail how to install nodes to your KNIME Analytics Platform.

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