class_2dim_unsup — SEGMENTATION classify op

• Data kinds: image2d × image2d → labels2d

• Call: import fullseye as fs; fs.ledger.class_2dim_unsup(image1, image2, n_clusters=3, iters=20) (to call the implementation directly, import segmentation; segmentation.class_2dim_unsup(image1, image2, n_clusters=3, iters=20); from the registry, opssegmentation.get("class_2dim_unsup"))

Usage

Unsupervised k-means classification in a 2-channel feature space; returns a label image (class_2dim_unsup).

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 · learn_ndim_norm · class_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.