dog — 2D edges op

• Data kinds: image → image

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

• HALCON equivalent: diff_of_gauss (the HALCON reference is a useful guide to its meaning and parameters)

Usage

Difference of Gaussians (DoG). Equivalent to HALCON's `diff_of_gauss` (Approximate the LoG operator (Laplace of Gaussian).).

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

`a が細かい方のぼかし σ₁ を 0.5〜2.5 に、b が粗い方のぼかし σ₂ を 1.0〜5.0 に振る（|gauss(σ₁) - gauss(σ₂)| を _norm` で正規化）。2 つのスケールの中間の大きさを持つ斑点・エッジを強調する、LoG の近似。σ₁ と σ₂ が近いほど応答は弱くなる。

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 · grad_dir · log · corner_response · sk_scharr · sk_farid

*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.