graph op• Data kinds: labels2d × labels2d → table
• Call: import fullseye as fs; fs.ledger.superpixel_quality(labels_sp, labels_true, *, tau: 'float' = 2.0) -> 'Dict[str, object]' (to call the implementation directly, import seggraph; seggraph.superpixel_quality(labels_sp, labels_true, *, tau: 'float' = 2.0) -> 'Dict[str, object]'; from the registry, opssegmentation.get("superpixel_quality"))
Superpixel scoring: boundary recall (the recall of segeval.seg_boundary_f, tolerance τ), the corrected undersegmentation error CUSE = (1/N) Σ_k |S_k − G_max(S_k)| (Neubert–Protzel 2012, eq. (2) of the SNIC paper), ASA = 1 − CUSE, the Neubert–Protzel undersegmentation error UE = (1/N) Σ_G Σ_(S∩G≠∅) min(|S∩G|, |S − G|), and the number of superpixels. Label 0 of the truth also counts as a region (the background must be covered too) (superpixel_quality).
• halcon_segmentation family guide
• graph_hierarchy_and_thresholds — Segmentation from graph, hierarchy and threshold theorems — what is exact, and where the partition breaks down
• 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_graph_hierarchy_segmentation — py -3.11 examples/poc_graph_hierarchy_segmentation.py
table as input)graph)graph_cut_binary · alpha_expansion · statistical_region_merging · max_tree · area_opening_attr · quasi_flat_zones · alpha_tree · hierarchical_watershed
*Provenance: seggraph.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.