smoothing op• Data kinds: image → image
• Call: fullseye.apply(img, "gauss_filter", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: gauss_filter (the HALCON reference is a useful guide to its meaning and parameters)
Smoothing via Gaussian blur (`scipy.ndimage.gaussian_filter, sigma 0.3+2.7*a). A stand-in for HALCON's gauss_filter` (Smooth using discrete Gauss functions.) -- HALCON uses a discrete Gaussian kernel (integer arithmetic), while this uses a continuous-Gaussian scipy implementation; it is an approximation, but the results are very close.
> The detailed description below is the original text — the summary and the headings are translated.
`a がシグマを 0.3〜3.0 の範囲で振る。b` は未使用。実装は
`gauss_image` と同一。
• 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
smoothing)gaussian · mean_box · bilateral · unsharp · sk_tv · sk_wavelet · sk_rolling_ball · sk_nlm
*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.