mean_curvature_flow — 2D `smoothing` op

• Data kinds: image → image

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

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

Usage

> This operator's description has not been translated yet. The original text follows as it is.

小さなシグマ(0.6)のガウシアンぼかしを繰り返し適用することで平均曲率流を

近似したスムージング。真の平均曲率 PDE(`ops` 側の別実装が本来やる

拡散方程式)ではなく、反復ぼかしという計算コストの軽い代用になっている点が

このバックエンド固有の近似の限界。HALCON の `mean_curvature_flow`

（Apply the mean curvature flow to an image.）の代役。

`a` が反復回数を 1〜7 回の範囲で振る(回数が多いほど強く滑らかになる)。

`b` は未使用。

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 (`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).*

mean_curvature_flow — 2D smoothing op