classify_image_class_lut — SEGMENTATION classify op

• Data kinds: image2d × signal → labels2d

• Call: import fullseye as fs; fs.ledger.classify_image_class_lut(image, lut) (to call the implementation directly, import segmentation; segmentation.classify_image_class_lut(image, lut); from the registry, opssegmentation.get("classify_image_class_lut"))

Usage

Pixel classification through a gray-value LUT (threshold / label table) (classify_image_class_lut).

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 labels2d as input)

watersheds_marker · seg_confusion_table · seg_dice_jaccard · seg_boundary_f · seg_hausdorff · seg_mean_surface_distance · seg_under_over_segmentation · seg_object_counts_match

Same category (classify)

class_2dim_sup · class_2dim_unsup · learn_ndim_norm · class_ndim_norm

*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.