Operator Reference
median_weighted (Operator)
median_weighted
— Weighted median filtering with different rank masks.
Signature
median_weighted(Image : ImageWMedian : MaskType, MaskSize : )
Description
The operator median_weighted
calculates the median of
the gray values within a local environment. In contrast to
median_image
, which uses all gray values within the
environment exactly once, the operator median_weighted
weights all gray values several times depending on their position.
A gray value is received into the field to be sorted several times
according to its weighting. The following masks are available:
The operator median_weighted
means that, contrary to
median_image
, gray value corners remain.
For an explanation of the concept of smoothing filters see the introduction of chapter Filters / Smoothing.
Attention
Note that filter operators may return unexpected results if an image with a reduced domain is used as input. Please refer to the chapter Filters.
Execution Information
- Multithreading type: reentrant (runs in parallel with non-exclusive operators).
- Multithreading scope: global (may be called from any thread).
- Automatically parallelized on tuple level.
- Automatically parallelized on channel level.
- Automatically parallelized on domain level.
Parameters
Image
(input_object) (multichannel-)image(-array) →
object (byte / int2 / uint2)
Image to be filtered.
ImageWMedian
(output_object) (multichannel-)image(-array) →
object (byte / int2 / uint2)
Median filtered image.
MaskType
(input_control) string →
(string)
Type of median mask.
Default: 'inner'
List of values: 'gauss' , 'inner'
MaskSize
(input_control) integer →
(integer)
mask size.
Default: 3
List of values: 3
Example (HDevelop)
read_image(Image,'fabrik') median_weighted(Image,MedianWeighted,'gauss',3) dev_display(MedianWeighted)
Complexity
For each pixel: O(F * log(F)) with F = area of MaskType
.
Possible Predecessors
Possible Successors
threshold
,
dyn_threshold
,
regiongrowing
Alternatives
median_image
,
trimmed_mean
,
sigma_image
References
R. Haralick, L. Shapiro; “Computer and Robot Vision”; Addison-Wesley, 1992, Seite 319
Module
Foundation