edt_jfa_vector — 3D `feature` op

• Data kinds: voxel → flow_dense

• Call: import fullseye as fs; fs.ledger.edt_jfa_vector(seed_bool, device='cpu') (to call the implementation directly, import match3d; match3d.edt_jfa_vector(seed_bool, device='cpu'); from the registry, ops3d.get("edt_jfa_vector"))

• Return value through the ledger: fullseye.ledger.edt_jfa_vector(...) returns only the declared out type flow_dense (the underlying function also returns auxiliary values). When you need what was dropped, use fullseye.ledger.edt_jfa_vector.raw(...) or call match3d.edt_jfa_vector directly.

• GPU: this op has a GPU path (device="cuda")

Usage

Displacement from each voxel to the nearest seed (3, D, H, W) (flow_dense, dz, dy, dx [voxel]), computed with GPU JFA.

edt_jfa returns only the distance values, but internally JFA carries the coordinates of the nearest seed —— this op outputs them as they are (almost 0 extra cost). The CPU / scipy path is vol_nearest_seed_vector (same values; exact match measured for N≤160). ValueError if there is no seed (edt_jfa saturates at 1e6, but a direction has no "infinitely far"). Returns torch float32 (numpy via the ledger).

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.

Runnable examples (verified samples that actually call this op)

• nearest_seed_partition — py -3.11 examples_3d/nearest_seed_partition.py

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

fuse_to_voxel

Same category (`feature`)

sobel3d · hessian3d · curvature_maps · edt_jfa · vol_frangi · vol_local_std · vol_local_thickness · vol_orientation_coherence

*Provenance: match3d.py — 3D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

edt_jfa_vector — 3D feature op