cv_gaussian — 2D `smoothing` op

• Data kinds: image → image

• Call: fullseye.apply(img, "cv_gaussian", 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)

Usage

Gaussian smoothing (OpenCV implementation). Standard blurring using a Gaussian kernel; less prone to ringing than a box filter.

> The detailed description below is the original text — the summary and the headings are translated.

HALCON の gauss_filter(Smooth using discrete Gauss functions.)に相当。実装は `cv2.GaussianBlur(v, (0,0), sigmaX=0.3+2.7*a) —— カーネルサイズを指定せず (0,0)` にすることで OpenCV に σ から自動算出させている。a は σ を0.3〜3.0 に振る。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).*

cv_gaussian — 2D smoothing op