seg_confusion_table — SEGMENTATION score op

• Data kinds: labels2d × labels2d → table

• Call: import fullseye as fs; fs.ledger.seg_confusion_table(labels_pred, labels_true, *, background: 'int' = 0) -> 'Dict[str, object]' (to call the implementation directly, import segeval; segeval.seg_confusion_table(labels_pred, labels_true, *, background: 'int' = 0) -> 'Dict[str, object]'; from the registry, opssegmentation.get("seg_confusion_table"))

Usage

Label-correspondence contingency table (rows = truth, columns = prediction) with max-overlap matching and a uniqueness flag (seg_confusion_table).

> The detailed description below is the original text — the summary and the headings are translated.

`table[i, j] = 真のラベル labels_true[i] と予測のラベル labels_pred[j]` を同時に持つ画素数。

対応は 最大重なり: 真 i の相手 = 背景でない列のうち `table[i, :] が最大の列(pred_of_true`、ラベル値、無ければ −1)、

予測 j の相手 = 背景でない行のうち最大の行(`true_of_pred)。mutual_true[i]` = 真 i の相手が真 i を選び返す、

`mutual_pred[j] も同様。claims[j] = 予測 j を相手に選んだ真の数。unique_matching` = 背景でない行・列がすべて相互に

選び合う(= 1 対 1 の全単射)。背景の行・列は表には残り、対応からは外す。

Hungarian(総和最大の割当)でなく argmax なので、1 つの大きな予測を複数の真が選ぶことがある —— それが `claims` ≥ 2 の印。

返り値: `table、labels_true / labels_pred(行・列のラベル値)、row_sums / col_sums、pred_of_true` /

`true_of_pred、mutual_true / mutual_pred、claims、unique_matching、n_true / n_pred(背景を除く個数)、n`。

Detailed usage guide

• halcon_segmentation family guide

Background guides (the physics and conventions behind this op)

• 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

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_dice_jaccard · seg_boundary_f · 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.