graph op• Data kinds: image2d → table
• Call: import fullseye as fs; fs.ledger.snic_superpixels(image, *, n_segments: 'int' = 100, compactness: 'float' = 0.1, connectivity: 'int' = 4) -> 'Dict[str, object]' (to call the implementation directly, import seggraph; seggraph.snic_superpixels(image, *, n_segments: 'int' = 100, compactness: 'float' = 0.1, connectivity: 'int' = 4) -> 'Dict[str, object]'; from the registry, opssegmentation.get("snic_superpixels"))
SNIC (Algorithm 1 of Achanta–Süsstrunk 2017): non-iterative superpixels grown from grid seeds with a priority queue (snic_superpixels).
Distance d² = |Δx|²/s² + |Δc|²/m² (s = √(N/K), m = compactness, c is the pixel value; the normalisation form of SLIC, the notation of eq. (1) is to be verified). Pops the smallest d from the priority queue; if unlabelled, labels it, updates the centroid (position and value) on the spot, and pushes the unlabelled neighbours with their distance to the current centroid. Ties are broken by (distance, push order) (deterministic).
Returns: labels (1..K), n_segments, all_connected (all labels are 4-connected = a flag confirming the guarantee by construction by counting), n_disconnected, sizes, seeds ([row, col] of (K, 2)), s.
• 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.