rank op• Data kinds: image → image
• Call: fullseye.apply(img, "median_separate", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: median_separate (the HALCON reference is a useful guide to its meaning and parameters)
The implementation is the same as `median_image (kind: "median", a 2D median over a square window). HALCON's median_separate` (Separated median filtering with rectangle masks.) is a fast approximation operator that applies two passes of 1D medians separated into row and column directions, but this stand-in makes no distinction and returns an ordinary 2D median (results are often close, but strictly it is a different algorithm -- a limitation of this approximation).
> The detailed description below is the original text — the summary and the headings are translated.
`a が窓の一辺を {3,5,7,9} で振る。b` は未使用。
• 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.
• 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.