threshold_kapur — SEGMENTATION `threshold` op

• Data kinds: image2d → table

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

Usage

Maximum-entropy threshold (Kapur–Sahoo–Wong 1985): the bin t that maximises H(t) = −Σ_(i ≤ t) (p_i/P1) ln(p_i/P1) − Σ_(i > t) (p_i/P2) ln(p_i/P2) (only bins where both sides have positive probability). Returns: `threshold = upper edge of that bin, mask (bin index > t), bin, entropy` (H, nan for excluded bins) (threshold_kapur).

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_kittler

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

threshold_kapur — SEGMENTATION threshold op