edges op• Data kinds: image → image
• Call: fullseye.apply(img, "dots_image", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: dots_image (the HALCON reference is a useful guide to its meaning and parameters)
A Laplacian of Gaussian (LoG) filter. Takes the Laplacian of a Gaussian kernel with sigma = 0.5+2.5a, normalized as signed01 (a signed value mapped to [0,1], with 0.5 = zero response). b is unused. Because circular dot-like patterns produce a strong LoG response due to the isotropy of the Gaussian, this is used to enhance circular dots.
> The detailed description below is the original text — the summary and the headings are translated.
HALCON の `dots_image`（画像中の円形ドットを強調する演算）に相当する近似で、専用のドット検出カーネルではなく一般的な LoG フィルタで代用している。
• 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.