typed op• Data kinds: signal → signal
• Call: fullseye.apply(img, "tb_stat_zscore", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
Standardise a 1-D sample: `(x - mean) / std (population ddof=0`).
The result has mean 0 and standard deviation 1 — the common currency for
comparing residuals across scales and flagging outliers (`|z| > 3`).
**A constant input raises `ValueError`** — the decision, stated: with
zero variance the z-score is 0/0. Returning silent zeros would claim "every
point is perfectly average", which is *a* convention but hides upstream
breakage (a sensor stuck at one value would sail through an outlier gate).
Fail-closed instead; a caller who wants the all-zeros convention can catch
this and substitute deliberately.
HALCON: no direct tuple operator (compose `tuple_mean` +
`tuple_deviation` + arithmetic).
Typed bridge of the math op `stat_zscore into the 2-D evolution registry: the same implementation, called under the op(v, a, b) convention. This op has no tunable parameter; a and b` are unused.
• 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.
• (none yet)
signal as input)identity · tb_create_funct_1d_array · tb_smooth_funct_1d_gauss · tb_smooth_funct_1d_mean · tb_derivate_funct_1d · tb_integrate_funct_1d · tb_zero_crossings_funct_1d · tb_abs_funct_1d
typed)tb_points_to_voxel · tb_estimate_point_normals · tb_iss_keypoints · tb_angle_3points · tb_project_points · tb_render_point_depth · tb_statistical_outlier_removal · tb_radius_outlier_removal
*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.