features op• Data kinds: image → image
• Call: fullseye.apply(img, "hough_circle_trans", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: hough_circle_trans (the HALCON reference is a useful guide to its meaning and parameters)
The Hough transform for circle detection. Computes `skimage.transform.hough_circle on the edge mask with a set of circle templates of radius 4 to 19 (in steps of 3), and returns the maximum response across all radii normalized to [0,1]. Corresponds to HALCON's hough_circle_trans` (Return the Hough-Transform for circles with a given radius.) (HALCON explicitly specifies the radius, but this is an approximation that brute-forces a fixed range).
> The detailed description below is the original text — the summary and the headings are translated.
`a がエッジ抽出の閾値を振る。b` は未使用 ―― 探索する半径レンジは
コード側に固定されており、`b` で半径を選ぶことはできない。
• gallery2d_features 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_features — py -3.11 examples/gallery2d_features.py
image as input)identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter
features)blob_count · area_frac · count_contours · total_length · vol_count · sk_euler · sk_entropy_feat · sk_blur_effect
*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.