seg_hausdorff — SEGMENTATION `score` op

• Data kinds: labels2d × labels2d → table

• Call: import fullseye as fs; fs.ledger.seg_hausdorff(labels_pred, labels_true, *, percentile: 'float' = 95.0) -> 'Dict[str, float]' (to call the implementation directly, import segeval; segeval.seg_hausdorff(labels_pred, labels_true, *, percentile: 'float' = 95.0) -> 'Dict[str, float]'; from the registry, opssegmentation.get("seg_hausdorff"))

Usage

Hausdorff distance between the two boundary pixel sets, with a percentile variant such as HD95 (seg_hausdorff).

Boundaries follow the convention of _boundary. If either boundary is empty (an image of 1 colour) there is nothing to measure, so ValueError.

Returns: hausdorff, hausdorff_percentile, percentile, directed_pred_to_true / directed_true_to_pred (each a maximum).

Detailed usage guide

• halcon_segmentation family guide

Background guides (the physics and conventions behind this op)

• segmentation_scoring_and_worlds — Scoring segmentations and synthetic worlds with ground truth — which metric is blind to which failure

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)

• (none yet)

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

class_ndim_norm

Same category (`score`)

seg_confusion_table · seg_dice_jaccard · seg_boundary_f · seg_mean_surface_distance · seg_under_over_segmentation · seg_object_counts_match · seg_score_card

*Provenance: segeval.py — SEGMENTATION operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

seg_hausdorff — SEGMENTATION score op