xcv2_meanshift — 2D `segmentation` op

• Data kinds: image → image

• Call: fullseye.apply(img, "xcv2_meanshift", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

xcv2_meanshift: 入力 → 出力

*The figure is the real output on a synthetic 128×128 input. Left: input, right: output (a non-image return value is shown as the value itself).*

Usage

Smooths color/brightness region by region using mean-shift filtering (`cv2.pyrMeanShiftFiltering`).

> The detailed description below is the original text — the summary and the headings are translated.

`a は空間窓半径 sp を 5〜30 に振り（sp = 5 + 25*a）、b` は

輝度（色）レンジ `sr を 10〜50 に振る（sr = 10 + 40*b`）。グレースケール

画像を内部で 3ch BGR に複製してから処理し、結果を再びグレースケールへ

戻す。エッジを保ったまま平坦な領域を単色化する前処理（減色・セグメンテー

ション前処理）に向く。

Detailed usage guide

• gallery2d_segmentation 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.

Try it in Studio

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.

xcv2_meanshift 0.50 0.50

▸ Load this pipeline · Load & run

Runnable examples (verified samples that actually call this op)

• gallery2d_segmentation — py -3.11 examples/gallery2d_segmentation.py

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

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

Same category (`segmentation`)

threshold · otsu · canny · adaptive_gauss_thresh · sk_otsu · sk_li · sk_yen · sk_sauvola

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

xcv2_meanshift — 2D segmentation op