threshold_kittler — SEGMENTATION `threshold` op

• Data kinds: image2d → table

• Call: import fullseye as fs; fs.ledger.threshold_kittler(image, *, nbins: 'int' = 256) -> 'Dict[str, object]' (to call the implementation directly, import seggraph; seggraph.threshold_kittler(image, *, nbins: 'int' = 256) -> 'Dict[str, object]'; from the registry, opssegmentation.get("threshold_kittler"))

Usage

Minimum-error threshold (Kittler–Illingworth 1986): at each bin t, splits into a low side (bins ≤ t) and a high side and minimises J(t) = 1 + 2(P1 ln σ1 + P2 ln σ2) − 2(P1 ln P1 + P2 ln P2) (σ is the standard deviation of the bin centres on each side; bins where either side has σ = 0 are excluded). Returns: `threshold = upper edge of that bin (the mask is decided by bin index > t = consistent at the edge values too), mask, bin, criterion` (J, nan for excluded bins) (threshold_kittler).

Detailed usage guide

• halcon_segmentation family guide

Background guides (the physics and conventions behind this op)

• graph_hierarchy_and_thresholds — グラフ・階層・閾値の定理でつくる分割 — 何が厳密で、どこで割れ方が倒れるか

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)

• poc_graph_hierarchy_segmentation — py -3.11 examples/poc_graph_hierarchy_segmentation.py

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

class_ndim_norm

Same category (`threshold`)

threshold_triangle · threshold_isodata · threshold_kapur

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

threshold_kittler — SEGMENTATION threshold op