sk_rolling_ball — 2D `smoothing` op

• Data kinds: image → image

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

Usage

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

ローリングボール法による背景差し引き。画像の輝度を地形と見立て、指定半径の球をその下から転がして得られる包絡面を背景と推定し、元画像から引き算する—— 照明ムラ(緩やかな輝度勾配)の除去に使う。

HALCON に直接対応するものは無い。実装は `v - restoration.rolling_ball(v, radius=5+int(a*20)) を [0,1]` へ clip したもの —— a は球の半径を 5〜25 に振る(大きいほど緩やかな照明ムラしか背景とみなさず、細かい構造は残る)。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_nlm · sk_tv_bregman

*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

sk_rolling_ball — 2D smoothing op