.. _`Image segmentation`:

.. _`com.sympathyfordata.imageanalysis.image_segmentation`:

Image segmentation
``````````````````

.. image:: image_segmentation.svg
   :width: 48


Segments an input color or grayscale image into regions
with integer labels




Documentation
:::::::::::::

Algorithms
==========

    **Chan-Vese**
        Active contour model based segmentation starting from an evolving level set. Can be used to segment objects without clearly defined boundaries.


        :iter: Maximum number of iterations of algoritm

        :mu: Edge length parameter. Higher values will produce rounder edges while smaller values will detect smaller objects

        :lambda1: Difference-from-average weight parameter. Affects the total area labelled positive

        :lambda2: Difference-from-average weight parameter. Affects the total area labelled negative

        :dt: Multiplicative factor speeding up calculation at risk for non-convergence

        :initial level set: Starting level set

        :morphological: If true then use morphological Chan-Vese instead




    **Felzenszwalb**
        Oversegmentation of a multichannel image based on minimum spanning trees on the image grid.


        :sigma: Standard deviation of gaussian kernel used in pre-processing (0.8)

        :scale: Observation level, higher number means larger clusters

        :min size: Minimum size of each component




    **K-means**
        Segments image using K-means clustering in color and spatial
        space.


        :n: Number of clusters for K-means

        :compactness: Balances color proximity and space proximity. Higher values
            give more weight to space proximity, making superpixel shapes
            more square/cubic. In SLICO mode, this is the initial
            compactness. This parameter depends strongly on image contrast
            and on the shapes of objects in the image. We recommend
            exploring possible values on a log scale, e.g., 0.01, 0.1,
            1, 10, 100, before refining around a chosen value.

        :iter: Maximum number of iterations of K-means

        :sigma: Width of a gaussian kernel used to smooth image before K-means

        :CIE Lab: If true (default) then image is converted to CIE-LAB colorspace
            before K-means, afterwards converted back to RGB. Image  must
            be a 3 channel RGB image

        :force connectivity: Forces the generated segments to be continous




    **Quickshift**
        Segments image using quickshift clustering in color and spatial space. Requires RGB images as inputs.


        :sigma: Width of a gaussian kernel used to smooth image before K-means

        :ratio: A value between 0.0 to 1.0. Balances between color and
            image space proximity

        :kernel size: Width of gaussian kernel smoothing sample density.
            Higher values mean fewer clusters.

        :max dist: Cut-off point for data distances. Higher means fewer clusters.

        :CIE Lab: If true (default) then image is converted to CIE-LAB colorspace
            before K-means. Image must be a 3 channel RGB image




    **Watershed**
        Floods watershed basins based on a set of N markers, suitable
        for grayscale images


        :n: Desired number of markers

        :compact: If not zero then use the compact-watershed algorithm
            giving more regularly shaped basins

        :line: Draws a one-pixel area with value=0 around each region


Definition
::::::::::


Input ports
===========

    **source**  image
        source image to segment


Output ports
============

    **result**  image
        result after segmentation




Configuration
=============

    **CIE Lab** (CIE Lab)
        (no description)
    **Algorithm** (algorithm)
        (no description)
    **compact** (compact)
        (no description)
    **compactness** (compactness)
        (no description)
    **dt** (dt)
        (no description)
    **force connectivity** (force connectivity)
        (no description)
    **initial level set** (initial level set)
        (no description)
    **iter** (iter)
        (no description)
    **kernel size** (kernel size)
        (no description)
    **lambda1** (lambda1)
        (no description)
    **lambda2** (lambda2)
        (no description)
    **line** (line)
        (no description)
    **max dist** (max dist)
        (no description)
    **min size** (min size)
        (no description)
    **morphological** (morphological)
        (no description)
    **mu** (mu)
        (no description)
    **n** (n)
        (no description)
    **ratio** (ratio)
        (no description)
    **scale** (scale)
        (no description)
    **sigma** (sigma)
        (no description)







Examples
========

* :download:`Depth_from_stereo_images.syx </Examples/Image Analysis/Depth_from_stereo_images.syx>`




Implementation
==============

.. automodule:: node_segmentation
    :noindex:

.. class:: Segmentation
    :noindex: