class_ndim_norm — SEGMENTATION classify op

• Data kinds: images × table → mask

• Call: import fullseye as fs; fs.ledger.class_ndim_norm(feature_images, model, thresh=3.0) (to call the implementation directly, import segmentation; segmentation.class_ndim_norm(feature_images, model, thresh=3.0); from the registry, opssegmentation.get("class_ndim_norm"))

Usage

Classify N-D feature images with a learned normal-distribution class (Mahalanobis distance < thresh) (class_ndim_norm).

Detailed usage guide

• halcon_segmentation 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)

• halcon_segmentation_tour — py -3.11 examples/halcon_segmentation_tour.py

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

class_2dim_sup · expand_gray · chan_vese_energy · chan_vese_evolve · morph_chan_vese · morph_geodesic_ac · level_set_reinit · drle_evolve

Same category (classify)

class_2dim_sup · class_2dim_unsup · learn_ndim_norm · classify_image_class_lut

*Provenance: segmentation.py — SEGMENTATION 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.