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 real output on a synthetic 128×128 input. Left: input, right: output (a non-image return value is shown as the value itself).*
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.
> The detailed description below is the original text — the summary and the headings are translated.
HALCON の `eliminate_min_max`（最小値・最大値の画素だけを周辺の平均値等で置き換えてノイズを抑える演算）とは異なり、この実装は最小・最大画素を選別せず窓全体を単純な中央値フィルタにかけているだけの近似（本来の「外れ値だけを直す」性質は再現しない）。
• 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.