features op• Data kinds: image → feature
• Call: fullseye.apply(img, "estimate_noise", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: estimate_noise (the HALCON reference is a useful guide to its meaning and parameters)
Robustly estimates the standard deviation σ of additive noise (`_noise_sigma: converts the MAD of the Laplacian response to a Gaussian-equivalent scale, then divides by the noise gain sqrt(20) of the 5-point Laplacian kernel). Designed to be insensitive to edge-derived outliers and pick up only the noise in flat areas (a bug in the old implementation, where "σ had no proper unit and saturated to 1.0 above σ≈0.08," was fixed on 2026-09-02 — see the _noise_sigma docstring for details). Corresponds to HALCON's estimate_noise` (Estimate the image noise from a single image.).
> The detailed description below is the original text — the summary and the headings are translated.
`a, b` は未使用。
• 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
feature as input)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.