xsp_gauss_grad_mag — 2D `edges` op

• Data kinds: image → image

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

Usage

Edge detection via the Gaussian gradient magnitude (`scipy.ndimage.gaussian_gradient_magnitude`).

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

`a はガウシアンの sigma を 0.5〜3.0 に振る（sigma = 0.5 + 2.5*a、大きいほど太い/滑らかなエッジになる）。b` は未使用。Sobel と違い先にガウシアンで平滑化してから勾配を取るのでノイズに強いが、sigma を大きくすると細部が失われる。

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).*

xsp_gauss_grad_mag — 2D edges op