smoothing op• Data kinds: image → image
• Call: fullseye.apply(img, "mean_curvature_flow", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
• HALCON equivalent: mean_curvature_flow (the HALCON reference is a useful guide to its meaning and parameters)
*The figure is the actual output on a synthetic 128×128 input. Left: input, right: output. Point clouds are drawn as a top-down scatter (brightness = z), 1-D series as a line plot, volumes as the maximum-intensity projection along z, videos as the middle frame, complex images as magnitude; return values that are not pictures are shown as the values themselves.*
*The output is shown in a viridis-like pseudo-colour (dark purple = low, yellow = high) so that a field of quantities — distance, phase, orientation, depth — can be read.*
Sweeping knob a (0.1 / 0.5 / 0.9, the other knob at its default):
▸ mean_curvature_flow: knob a sweep (docs site)
*Knob b does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ mean_curvature_flow: other inputs (docs site)
*No colour (H,W,3) input is shown: this op treats the colour channel as a third spatial axis (it crosses channels). Call it per channel.*
Smoothing that approximates mean curvature flow by repeatedly applying a small-sigma (0.6) Gaussian blur. Rather than the true mean-curvature PDE (the diffusion equation that a separate implementation on the `ops side actually performs), it substitutes computationally cheap iterative blurring -- a limitation specific to this backend's approximation. A stand-in for HALCON's mean_curvature_flow` (Apply the mean curvature flow to an image.).
> The detailed description below is the original text — the summary and the headings are translated.
`a` が反復回数を 1〜7 回の範囲で振る(回数が多いほど強く滑らかになる)。
`b` は未使用。
Border handling: mirror repeating the edge pixel (d c b a | a b c d — scipy's default `reflect) (measured; tools/impl2/border_probe.py`).
Knobs (measured): `a` acts in discrete steps; it switches at a ≈ 0.17、0.33、0.49、0.67、0.83、0.99 (measured on a sweep of step 0.02).
• gallery2d_smoothing_rank family guide
• 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.
The program below has been verified to run (same input as the figure). In Studio's help this block becomes buttons that load and run it on the spot.
mean_curvature_flow 0.35 0.50
▸ Load this pipeline · Load & run
• gallery2d_smoothing_rank — py -3.11 examples/gallery2d_smoothing_rank.py
image as input)identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter
smoothing)gaussian · mean_box · bilateral · unsharp · sk_tv · sk_wavelet · sk_rolling_ball · sk_nlm
*Provenance: ops.py — 2D 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.