xsp_morph_laplace — 2D `edges` op

• Data kinds: image → image

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

Usage

Edge enhancement via the morphological Laplacian (`scipy.ndimage.morphological_laplace`).

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

`a は構造要素のサイズを 3, 5, 7, 9 に振る（size = 3 + 2*int(a*3)）。b は未使用。グレースケール膨張と収縮の差の差というべき演算で、通常の（線形）ラプラシアンよりノイズに敏感な代わりに輪郭を鋭く拾う。符号付きの結果を signed01` で [0,1] に写像するので 0.5 が無変化（ゼロ交差）にあたる。

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_morph_laplace — 2D edges op