median_image — Compute a median filter with various masks.
median_image(Image : ImageMedian : MaskType, Radius, Margin : )
median_image performs a median filter on the input image
Image with a square or circular mask and returns the
filtered image in ImageMedian. The shape of the mask can
be selected with MaskType. The radius of the mask can be
selected with Radius.
Conceptually, the median filter sorts all gray values within the
mask in ascending order and then selects the median of the gray
values. The median is the “middle” one of the sorted gray values,
i.e., the gray value with rank (position) (N - 1) / 2 + 1 of the
sorted gray values, where N denotes the number of pixels covered by
the filter mask.
Here, the rank 1 corresponds to the smallest gray value and the rank N
corresponds to the largest gray value within the mask (see also
rank_image).
The filter mask is determined by Radius, defining the size, and
MaskType, defining the shape of the mask.
For latter one, the following options are available:
The mask consists of the pixel within a circle
with Radius around the pixel of the mask center.
The mask consists of the pixel within a square
with an edge length of
Radius pixel.
median_image can be used, for example, to smooth images, to
suppress unwanted objects (e.g., point-like or line-like structures)
that are smaller than the mask, and can therefore be used to
estimate the background illumination for a shading correction or as
a preprocessing step for the dynamic threshold operation (see
dyn_threshold).
Several border treatments can be chosen for filtering via the
parameter Margin:
Pixels outside of the image border are assumed to be constant (with the indicated gray value).
Continuation of the gray values at the image border.
Cyclic continuation at the image borders.
Reflection of pixels at the image borders.
When using the MaskType 'square' with Radius 1 or 2
(resulting in a 3x3 or 5x5 pixel filter mask) and the border treatment
'mirrored', median_image can be executed on OpenCL devices.
For an explanation of the concept of smoothing filters see the introduction of chapter Filters / Smoothing.
median_image uses an algorithm with a runtime per pixel that
depends on the mask height 2 *
Radius + 1. Therefore, median_image is slower
than median_rect for square masks with a large mask height.
The precise mask height for which median_rect will become
faster than median_image depends on the computer
architecture (processor type, availability of SIMD instructions like
SSE2 or MMX, cache size and throughput, memory throughput).
Typically, this is the case for mask heights > 15, but can also be
the case only for larger mask sizes, e.g., if SIMD instructions are
unavailable and memory throughput is low.
Furthermore, it should be noted that median_rect uses a
recursive implementation, which internally computes the filter
response on the smallest enclosing rectangle of the domain of the
input image. Therefore, if the domain of the input image only
covers a small fraction of the smallest enclosing rectangle, it can
happen that median_image is faster than median_rect
even for larger mask heights.
Due to performance reasons, the input Image is not checked whether
it contains NaNs. Using an input image with NaNs crashes HALCON.
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.
Image (input_object) (multichannel-)image(-array) → object (byte* / int2* / uint2* / int4* / real*) *allowed for compute devices
Image to be filtered.
ImageMedian (output_object) (multichannel-)image(-array) → object (byte / int2 / uint2 / int4 / real)
Filtered image.
MaskType (input_control) string → (string)
Filter mask type.
Default: 'circle'
List of values: 'circle', 'square'
List of values (for compute devices): 'square'
Radius (input_control) integer → (integer)
Radius of the filter mask.
Default: 1
List of values (for compute devices): 1, 2
Suggested values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 15, 19, 25, 31, 39, 47, 59
Value range:
1
≤
Radius
≤
4095
Minimum increment: 1
Recommended increment: 2
Margin (input_control) string → (string / integer / real)
Border treatment.
Default: 'mirrored'
List of values (for compute devices): 'mirrored'
Suggested values: 'mirrored', 'cyclic', 'continued', 0, 30, 60, 90, 120, 150, 180, 210, 240, 255
read_image (Image, 'fabrik') median_image (Image, Median, 'circle', 3, 'continued') dev_display(Median)
For each pixel: O(2 *
Radius + 1).
If the parameter values are correct the operator
median_image returns the value 2 (
H_MSG_TRUE)
. The behavior in case
of empty input (no input images available) is set via the operator
set_system('no_object_result',<Result>). If necessary, an
exception is raised.
threshold,
dyn_threshold,
regiongrowing
median_rect,
rank_image,
rank_rect
gray_erosion_rect,
gray_dilation_rect,
gray_erosion_shape,
gray_dilation_shape,
gray_erosion,
gray_dilation
T.S. Huang, G.J. Yang, G.Y. Tang; “A Fast Two-Dimensional Median
Filtering Algorithm”; IEEE Transactions on Acoustics, Speech, and
Signal Processing, vol. 27, no. 1, pp. 13-18, 1979.
R. Haralick, L. Shapiro; “Computer and Robot Vision”;
Addison-Wesley, 1992, pp. 318-320.
Foundation