trimmed_mean — 2D `rank` op

• Data kinds: image → image

• Call: fullseye.apply(img, "trimmed_mean", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

• HALCON equivalent: trimmed_mean (the HALCON reference is a useful guide to its meaning and parameters)

Usage

Approximates a trimmed mean by returning the average of the 20th- and 80th-percentile filter results. This yields a value close to an average with outlier influence (from the low and high extremes) removed, but note that it is an approximation whose computation differs from a true "average the central 60%" trimmed mean. A stand-in for HALCON's `trimmed_mean` (Smooth an image with an arbitrary rank mask.).

> 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(トリム比率は 20/80

に固定)。

Detailed usage guide

• gallery2d_smoothing_rank family guide

References (sample data, literature)

• 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.

Runnable examples (verified samples that actually call this op)

• gallery2d_smoothing_rank — py -3.11 examples/gallery2d_smoothing_rank.py

Ops the type connects to (they accept `image` as input)

identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter

Same category (`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).*

trimmed_mean — 2D rank op