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])

xsp_morph_laplace: 入力 → 出力

*The figure is the real output on a synthetic 128×128 input. Left: input, right: output (a non-image return value is shown as the value itself).*

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.

Try it in Studio

The program below has been verified to run (same input as the figure). In Studio's help this block becomes buttons that load and run it on the spot.

xsp_morph_laplace 0.40 0.50

▸ Load this pipeline · Load & run

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