derivate_gauss — 2D edges op

• Data kinds: image → image

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

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

Usage

The gradient magnitude (`hypot) of Gaussian derivatives (order=(1,0) and (0,1)), normalized to [0,1]. Because it differentiates after Gaussian smoothing, it acts as a noise-robust edge detector. Equivalent to HALCON's derivate_gauss` (Convolve an image with derivatives of the 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(平滑化の強さとエッジの

太さがトレードオフ)。`b` は未使用。方向別成分(dx, dy)ではなく振幅のみを

返す点に注意。

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).*

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.