feature op• Data kinds: voxel → voxel
• Call: import fullseye as fs; fs.ledger.vol_local_std(vol, size=5) (to call the implementation directly, import volops; volops.vol_local_std(vol, size=5); from the registry, ops3d.get("vol_local_std"))
Unbiased local standard deviation inside a cubic window.
The 3-D counterpart of the registry op local_std (HALCON's deviation_image). The variance is taken after subtracting the global mean — E[x^2] - E[x]^2 loses every significant digit on a volume whose values sit far from zero, and the leftover shows up as a fake texture on a uniform block. It is then unbiased twice: n/(n-1) on the variance and c4(n) on the square root, so the estimate of sigma itself (not of sigma^2) is unbiased.
*size* is the edge of the cubic window in voxels (odd, >= 3), so n = size**3 voxels enter each estimate. The relative standard error of a single voxel's estimate is 1/sqrt(2(n-1)) — 3.1% for a 5x5x5 window, 1.1% for 9x9x9. Quote that figure whenever a noise or roughness number is read off this volume.
Returns a (D, H, W) float64 volume in the input's own units (it is *not* normalised, so values from different scans compare directly).
• 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.
• ct_porosity_and_fibre_morphometry — py -3.11 examples_3d/ct_porosity_and_fibre_morphometry.py
voxel as input)voxel_to_mips · voxel_to_mesh · signed_distance_field · to_points · sobel3d · hessian3d · curvature_maps · edt_jfa
feature)sobel3d · hessian3d · curvature_maps · edt_jfa · edt_jfa_vector · vol_frangi · vol_local_thickness · vol_orientation_coherence
*Provenance: volops.py — 3D 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.