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)
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.
• 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.