List of Operators ↓
This chapter describes threshold operators.
One way to perform a segmentation of an image is to use threshold operators. In doing so, regions that fulfill a certain threshold condition, depending on gray values, are determined within an image.
In order to fit different tasks and image properties there is a set of threshold operators provided.
The following paragraphs give an overview of the operators by differentiating them into histogram-based and local methods and having a closer look at the most important ones.
Histogram-based threshold segmentation does not take the position but only the pixel value into account. Therefore thresholds are determined by adjusting them to the histogram of an image.
, you can select all pixels within user set gray
also works with two manually determined
thresholds but uses another computing algorithm.
To divide your image into light and dark regions,
automatically computes a threshold to separate foreground from background.
A segmentation using
also divides the image into
foreground and background, but puts out the separating border with sub-pixel
accuracy. The threshold has to be set manually.
computes local minima in the image histogram to
determine thresholds. By smoothing the histogram you have influence on the
number of classes found in the input image.
to get the gray values of local minima in the
To do a segmentation of dark text on light background,
is a useful tool. The maximum peak in the histogram corresponds to the
light background. Assuming the text is darker than the background, you
examine the smoothed histogram left of the maximum. The parameter
determines how far from the maximum the threshold is set,
taking the frequencies of the gray values into account.
Subtracting an image from another or using an edge detecting
usually leads to negative values in
the resulting image. The operator
is suited for the
segmentation of signed images while also taking minimum region size into
Unlike histogram-based threshold operators, a local threshold also takes position or neighborhood of pixels into account to assign them to the appropriate region. Instead of global thresholds that are applied to each pixel, it is sometimes be useful to adapt the threshold to local features of the image.
takes the local mean and standard deviation into
account and computes an individual threshold for each pixel. The size of the
neighborhood is set by the user.
This operator is especially suited for the segmentation of text, when
lighting conditions or the background are not homogeneous.
works in a similar way, except it selects
those points of the image that fulfill specified conditions regarding their
local standard deviation and brightness.
you can inspect the differences between images.
Usually the input image and a filtered version, e.g., the mean of the
image, are compared pixel by pixel. The parameter
to determine what kind of changes in the image are relevant. The sensitivity
of the operator is controlled by the parameter
A similar operator is
. The operator displays the
absolute differences between two images. It is especially suited for
change detection of consecutively acquired images.
that are used for edge detection
return signed images, where edges are located at the zero crossings.
can be used to
extract those edges, taking the individual 4-neighborhood into account.