edges op• Data kinds: image → image
• Call: fullseye.apply(img, "laplace_of_gauss", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: laplace_of_gauss (the HALCON reference is a useful guide to its meaning and parameters)
*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).*
LoG (Laplacian of Gaussian; second-derivative edge detection obtained by applying a Laplacian after Gaussian smoothing). `signed01 maps the signed response to [0,1] (0.5 corresponds to a zero crossing), so it can serve as a basis for zero-crossing detection or blob detection. Equivalent to HALCON's laplace_of_gauss` (LoG-Operator (Laplace of Gaussian).).
> The detailed description below is the original text — the summary and the headings are translated.
`a がガウス核のシグマを 0.5〜3.0 の範囲で振る。b` は未使用。値が 0.5
から離れるほど強いエッジ/ブロブ応答であることを示す。
• gallery2d_edges family guide
• 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.
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.
laplace_of_gauss 0.40 0.50
▸ Load this pipeline · Load & run
• gallery2d_edges — py -3.11 examples/gallery2d_edges.py
image as input)identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter
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).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.