rank op• Data kinds: image → image
• Call: fullseye.apply(img, "eliminate_min_max", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: eliminate_min_max (the HALCON reference is a useful guide to its meaning and parameters)
*The figure is the actual output on a synthetic 128×128 input. Left: input, right: output. Point clouds are drawn as a top-down scatter (brightness = z), 1-D series as a line plot, volumes as the maximum-intensity projection along z, videos as the middle frame, complex images as magnitude; return values that are not pictures are shown as the values themselves.*
Sweeping knob a (0.1 / 0.5 / 0.9, the other knob at its default):
▸ eliminate_min_max: knob a sweep (docs site)
*Knob b does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ eliminate_min_max: other inputs (docs site)
*No colour (H,W,3) input is shown: this op treats the colour channel as a third spatial axis (it crosses channels). Call it per channel.*
Smoothing via a median filter. The implementation shares the median branch of _sh_rank with ops like median_image, simply replacing each pixel with the median of its window. a sets the window size to one of 3/5/7/9 (_k(a)). b is unused.
Unlike HALCON's eliminate_min_max (an operation that suppresses noise by replacing only the minimum and maximum pixels with the mean of their surroundings, etc.), this implementation does not single out the minimum/maximum pixels but merely applies a plain median filter to the whole window -- an approximation (the original property of "fixing only the outliers" is not reproduced).
Border handling: mirror repeating the edge pixel (d c b a | a b c d — scipy's default reflect) (measured; tools/impl2/border_probe.py).
• gallery2d_smoothing_rank family guide
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
The program below has been verified to run (same input as the figure). In Studio's help this block becomes buttons that load and run it on the spot.
eliminate_min_max 0.50 0.50
▸ Load this pipeline · Load & run
• gallery2d_smoothing_rank — py -3.11 examples/gallery2d_smoothing_rank.py
image as input)identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter
rank)median · min_filter · max_filter · percentile · sk_median_disk · cv_median · median_image · median_rect
*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.