rank_image — Compute a rank filter with arbitrary masks.
rank_image performs a rank filter on the input image Image with a mask that is specified by the region Mask and returns the filtered image in ImageRank. The shape of the mask can be chosen arbitrarily and can, for example, be created with operators like gen_circle or draw_region. The position of the mask does not influence the result since the center of gravity of the mask region is used as the reference point of the mask.
Conceptually, the rank filter sorts all gray values within the mask in ascending order and then selects the gray value with rank Rank. The rank 1 corresponds to the smallest gray value and the rank A corresponds to the largest gray value within the mask. Here, A denotes the area of Mask (see area_center). For Rank = (A - 1) / 2 + 1, rank_image returns the median gray value (see median_image). For Rank = 1, rank_image performs a gray value erosion (see gray_erosion_rect, gray_erosion_shape, and gray_erosion), while for Rank = A rank_image performs a gray value dilation (see gray_dilation_rect, gray_dilation_shape, and gray_dilation).
rank_image can be used, for example, to suppress noise or to suppress unwanted objects that are smaller than the mask. Furthermore, rank_image is less sensitive to noise than the correponding gray value morphology operators. Therefore, to obtain a more robust version of the gray value morphology, instead of using 1 or A, slightly larger or smaller values should be selected for Rank.
Several border treatments can be chosen for filtering via the parameter Margin:
gray value Pixels outside of the image borders are assumed to be constant (with the specified gray value). 'continued' Continuation of border pixels. 'cyclic' Cyclic continuation of image borders. 'mirrored' Reflection of pixels at the image borders.
rank_image uses an algorithm with a runtime per pixel that depends on the number of runs in the mask Mask. Therefore, rank_image is slower than rank_rect for rectangular masks with a large mask height. The precise mask height for which rank_rect will become faster than rank_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 rank_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 rank_image is faster than rank_rect even for larger mask heights.
rank_image should neither be used with Rank = 1 to perform a gray value erosion nor with Rank = A to perform a gray value dilation. In these cases, the operators gray_erosion_rect, gray_erosion_shape, or gray_erosion and gray_dilation_rect, gray_dilation_shape, or gray_dilation, respectively, are typically faster than rank_image.
Image to be filtered.
Rank of the output gray value.
Default value: 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: 1 ≤ Rank ≤ 4095
Minimum increment: 1
Recommended increment: 2
Default value: 'mirrored'
Suggested values: 'mirrored', 'cyclic', 'continued', 0, 30, 60, 90, 120, 150, 180, 210, 240, 255
read_image (Image, 'fabrik') draw_region (Region, WindowHandle) rank_image (Image, Region, ImageRank, 5, 'mirrored') dev_display(ImageRank)
For each pixel: O(N), where N is the number of runs of Mask (see runlength_features).
If the parameter values are correct the operator rank_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.
read_image, draw_region, gen_circle, gen_rectangle1, gen_rectangle2
threshold, dyn_threshold, regiongrowing
rank_rect, median_image, median_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.