typed op• Data kinds: matrix → matrix
• Call: fullseye.apply(img, "tb_stat_covariance", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
*No figure: this op takes matrix as input. A Studio program starting from an image cannot reach that type — see the runnable examples below for how it is used.*
Sample covariance matrix of `(N, D) observations → (D, D)`.
Rows are observations, columns are variables — the `(N, D)` orientation
every Fullseye point/sample API uses (note `np.cov` defaults to the
*transposed* convention). Uses the unbiased `ddof=1` estimator (divides
by `N - 1), hence the N >= 2` requirement. The diagonal holds the
per-variable sample variances; the result is symmetric positive
semi-definite by construction, so it can go straight into
:func:mat_eigh for principal axes (the covariance-ellipse workflow).
HALCON: no public tuple/matrix operator — covariance lives inside HALCON's
calibration and matching internals only.
Typed bridge of the math op `stat_covariance 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)
matrix as input)identity · tb_mat_pinv · tb_mat_cond · tb_stat_correlation
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.