Determine the entropy and anisotropy of images.
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:
255
----
\
Entropy = - / rel[i] * ld(rel[i])
----
0
Anisotropiy coefficient:
k
----
\
/ rel[i] * ld(rel[i])
----
0
Anisotropy = -----------------------------
Entropy
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
|
Regions (input_object) |
region(-array) -> object |
| Regions where the features are to be determined. | |
|
Image (input_object) |
image -> 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 handling is raised.
entropy_gray is reentrant and automatically parallelized (on tuple level).
entropy_image, gray_histo, gray_histo_abs, fuzzy_entropy, fuzzy_perimeter
Image filters
