feat_image_vector — MULTIVAR features op

• Data kinds: images → table

• Call: import fullseye as fs; fs.ledger.feat_image_vector(images=None, features=('mean', 'std', 'p01', 'p99', 'rms_contrast', 'michelson_contrast', 'grad_energy', 'laplacian_var'), data_range=None, unit='DN', roi=None) -> 'dict' (to call the implementation directly, import multivar; multivar.feat_image_vector(images=None, features=('mean', 'std', 'p01', 'p99', 'rms_contrast', 'michelson_contrast', 'grad_energy', 'laplacian_var'), data_range=None, unit='DN', roi=None) -> 'dict'; from the registry, opsmultivar.get("feat_image_vector"))

Usage

Images -> feature table {"matrix" (N, F), "names", "units", ...}, one row per image.

The bridge from pictures to the multivariate ops: each image becomes a row of named numbers with units, ready for mv_pca / mv_pls / rsm_fit. Features (see FEATURES): mean, std, p01, p50, p99, rms_contrast, michelson_contrast, grad_energy, laplacian_var need no range; saturated_fraction, dark_fraction, entropy_bits need data_range=(lo, hi) — the range of the *sensor or format* (e.g. (0, 4095) for 12-bit), which the data cannot tell: the brightest pixel of a dim image is not the white level. It is therefore required, never inferred, and pixels outside it are refused (the range given is then wrong).

unit names the grey-level unit ("DN" by default) used in the unit strings. roi=(r0, r1, c0, c1) restricts every feature to that window (same for all images).

Detailed usage guide

• design_of_experiments 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)

• multivar_inspection_tour — py -3.11 examples/multivar_inspection_tour.py

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

—

Same category (features)

—

*Provenance: multivar.py — MULTIVAR 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.