score op• Data kinds: labels2d × labels2d → table
• Call: import fullseye as fs; fs.ledger.seg_boundary_f(labels_pred, labels_true, *, tau: 'float' = 2.0) -> 'Dict[str, object]' (to call the implementation directly, import segeval; segeval.seg_boundary_f(labels_pred, labels_true, *, tau: 'float' = 2.0) -> 'Dict[str, object]'; from the registry, opssegmentation.get("seg_boundary_f"))
Boundary F-measure at tolerance tau: precision = fraction of predicted boundary pixels within tau of a true boundary, recall the reverse (seg_boundary_f).
> The detailed description below is the original text — the summary and the headings are translated.
F = 2PR/(P+R)。境界は :func:_boundary の規約(ラベルの割れ目、1 画素幅)なので、ラベルの番号や背景の区別によらない
(分割の形だけを見る)。距離は距離変換(ユークリッド、画素の中心)。τ = 0 で画素の一致。
両方の境界が空(どちらも 1 色)なら P = R = F = 1、片方だけ空なら 0。
返り値: `f、precision、recall、tau、n_pred_boundary / n_true_boundary`、
`dist_pred_to_true / dist_true_to_pred`(境界画素ごとの距離)。
• halcon_segmentation family guide
• segmentation_scoring_and_worlds — 分割の採点と真値つき合成世界 — どの物差しがどの壊れ方に盲目か
• 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.
• poc_am_thermal_to_ct — py -3.11 examples/poc_am_thermal_to_ct.py
• poc_graph_hierarchy_segmentation — py -3.11 examples/poc_graph_hierarchy_segmentation.py
table as input)score)seg_confusion_table · seg_dice_jaccard · seg_hausdorff · 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).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.