tf_rank_transform — 2D `texture` op

• Data kinds: image → image

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

Usage

rank_transform: the local rank of each pixel among its neighbours.

Each pixel's value is the fraction of neighbours in a `(2r+1)x(2r+1)` window

that it strictly exceeds (`r = 1 + round(a*2); b` unused), i.e. its

ordinal rank normalized to [0,1]. Like the census transform it depends only on

pixel ordering, so it is invariant to a global gain: multiplying the image by

any positive constant leaves every rank unchanged (robust to illumination gain

for stereo/texture).

Detailed usage guide

• gallery2d_texture_freq 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_texture_freq — py -3.11 examples/gallery2d_texture_freq.py

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

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

Same category (`texture`)

std_filter · gabor · sk_frangi · sk_meijering · sk_hessian · sk_gabor · sk_lbp · sk_entropy

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

tf_rank_transform — 2D texture op