entropy_gray — Determine the entropy and anisotropy of images.
entropy_gray(Regions, Image : : : Entropy, Anisotropy)
The operator entropy_gray creates the histogram of
relative frequencies of the gray values in the input image and
calculates from these frequencies the entropy and the anisotropy
coefficient for each region from Regions according to the
following formulae:
Entropy: Anisotropiy coefficient: where rel[i] histogram of relative gray value frequencies i Gray value of input image (0..255) and k Smallest possible gray value with sum(rel[i]) >= 0.5
Note that the operator entropy_gray only considers
the given Regions and ignores any previously set domain
of the input image Image.
Regions (input_object) region(-array) → object
Regions where the features are to be determined.
Image (input_object) singlechannelimage → object (byte)
Gray value image.
Entropy (output_control) real(-array) → (real)
Information content (entropy) of the gray values.
Assertion: 0 <= Entropy && Entropy <= 8
Anisotropy (output_control) real(-array) → (real)
Measure of the symmetry of gray value distribution.
If F is the area of the region the runtime complexity is O(F + 255).
The operator entropy_gray returns the value 2 (H_MSG_TRUE) if an
image with defined gray values is entered and the parameters are
correct. The behavior in case of empty input (no input images
available) is set via the operator
set_system(::'no_object_result',<Result>:), the behavior
in case of empty region is set via
set_system(::'empty_region_result',<Result>:).
If necessary an exception is raised.
entropy_image,
gray_histo,
gray_histo_abs,
fuzzy_entropy,
fuzzy_perimeter
Foundation