| Operators |
median_rect — Compute a median filter with rectangular masks.
median_rect(Image : ImageMedian : MaskWidth, MaskHeight : )
median_rect performs a median filter on the input image Image with a rectangular mask of size MaskWidth × MaskHeight and returns the filtered image in ImageMedian.
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) (MaskWidth * MaskHeight - 1) / 2 + 1 of the sorted gray values, where the rank 1 corresponds to the smallest gray value and the rank MaskWidth * MaskHeight corresponds to the largest gray value within the mask (see also rank_rect).
median_rect 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).
When using a 3x3 or 5x5 filter mask, median_rect can be executed on OpenCL devices.
For an explanation of the concept of smoothing filters see the introduction of chapter Filters / Smoothing.
If even values instead of odd values are passed in MaskHeight or MaskWidth, median_rect uses the next larger odd values instead.
median_rect uses an algorithm with constant runtime per pixel, i.e., the runtime only depends on the size of the input image and not on the mask size. Therefore, for large mask sizes median_rect is the fastest implementation of the median filter in HALCON. Depending on the computer architecture (processor type, availability of SIMD instructions like SSE2 or MMX, cache size and throughput, memory throughput), for small mask sizes the implementation used in median_image and rank_image is faster than median_rect. Typically, this is the case for MaskHeight 15, but can also happen 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 and rank_image are faster than median_rect even for larger values of MaskHeight.
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 to be filtered.
Filtered image.
Width of the filter mask.
Default value: 15
List of values (for compute devices): 3, 5
Suggested values: 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 31, 49, 51, 61, 71, 81, 91, 101
Typical range of values: 3 ≤ MaskWidth ≤ 4095
Minimum increment: 2
Recommended increment: 2
Height of the filter mask.
Default value: 15
List of values (for compute devices): 3, 5
Suggested values: 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 31, 49, 51, 61, 71, 81, 91, 101
Typical range of values: 3 ≤ MaskHeight ≤ 4095
Minimum increment: 2
Recommended increment: 2
For each pixel: O(1).
If the parameter values are correct the operator median_rect returns the value 2 (H_MSG_TRUE). The behavior in case of empty input (no input images available) is set via set_system('no_object_result',<Result>). If necessary, an exception is raised.
threshold, dyn_threshold, regiongrowing
median_image, rank_rect, rank_image
gray_erosion_rect, gray_dilation_rect, gray_erosion_shape, gray_dilation_shape, gray_erosion, gray_dilation
S. Perreault, P. Hébert; “Median Filtering in Constant Time”;
IEEE Transactions on Image Processing vol. 16, no. 9, pp. 2389-2394,
2007.
D. Cline, K.B. White, P.K. Egbert; “Fast 8-Bit Median Filtering
Based On Separability”; International Conference on Image
Processing, vol. V, pp. 281-284, 2007.
Foundation
| Operators |