alife_curvature_flow — 2D `artificial-life` op

• Data kinds: image → image

• Call: fullseye.apply(img, "alife_curvature_flow", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

Usage

Mean-curvature motion / level-set smoothing (curve-shortening flow).

Integrates u_t = |grad u| * div(grad u / |grad u|) in the numerically stable

algebraic form

u_t = (u_xx u_y^2 - 2 u_x u_y u_xy + u_yy u_x^2) / (u_x^2 + u_y^2 + eps),

i.e. every level curve of the image moves along its normal at a speed equal

to its own curvature: small blobs and boundary wiggles vanish, straight edges

stay put. `a sets the number of steps 1 + int(29a), b` the time step

dt = 0.05 + 0.2b.

Detailed usage guide

• gallery2d_physics_alife_3d family guide

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.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• gallery2d_physics_alife_3d — py -3.11 examples/gallery2d_physics_alife_3d.py

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

identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter

Same category (`artificial-life`)

alife_gray_scott · alife_turing · alife_life_step · alife_cyclic_ca · alife_perona_malik · alife_dla · alife_reaction_bz · alife_wolfram1d

*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

alife_curvature_flow — 2D artificial-life op