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)
*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).*
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。
• 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.
derivate_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.