sk_dog — 2D `edges` op

• Data kinds: image → image

• Call: fullseye.apply(img, "sk_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

DoG (Difference of Gaussians) filter. Takes the difference between images blurred with two different sigma values, cheaply obtaining a bandpass response close to LoG (Laplacian of Gaussian) - edge/blob detection that emphasizes only a specific spatial-frequency band.

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

HALCON の diff_of_gauss(Approximate the LoG operator.)に相当。実装は `filters.difference_of_gaussians(v, 1.0, 1.0+3.0*a)` の絶対値を正規化したもの —— 小さい方の σ は 1.0 に固定し、a は大きい方の σ を 1.0〜4.0 に振る(σ の比が広がるほど検出する構造のスケール帯が広がる)。b は未使用。

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).*

sk_dog — 2D edges op