xg_regress_contours — 2D xldgeom op

• Data kinds: contour → feature

• Call: fullseye.apply(img, "xg_regress_contours", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

Usage

Total-least-squares line residual RMS = sqrt(minor covariance eigenvalue).

The perpendicular (orthogonal-regression) residual variance of a point set

equals its smallest covariance eigenvalue; its square root is the RMS

perpendicular distance to the best-fit line.

Detailed usage guide

• gallery2d_geometry family guide

References (sample data, literature)

• 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.

Runnable examples (verified samples that actually call this op)

• gallery2d_geometry — py -3.11 examples/gallery2d_geometry.py

Ops the type connects to (they accept feature as input)

identity

Same category (xldgeom)

xg_moments · xg_area_center · xg_eccentricity · xg_orientation · xg_elliptic_axis · xg_height_width_ratio · xg_clip_contours · xg_gen_polygons

*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.