cv_laplacian — 2D `edges` op

• Data kinds: image → image

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

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

Usage

Laplacian filter (OpenCV implementation). Isotropic edge/blob detection using the second derivative; it responds strongly not only to contours but also to isolated points (and is thus sensitive to noise).

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

HALCON の laplace(Calculate the Laplace operator by using finite differences.)に相当。実装は `|cv2.Laplacian(v, CV_64F)|` を正規化したもの —— カーネルサイズは既定の 1(3x3 の基本 4 近傍ラプラシアンカーネル)に固定。a, b は未使用。

Detailed usage guide

• gallery2d_edges 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_edges — py -3.11 examples/gallery2d_edges.py

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

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

Same category (`edges`)

sobel_mag · prewitt_mag · roberts_mag · dog · grad_dir · log · corner_response · sk_scharr

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

cv_laplacian — 2D edges op