tb_statistical_outlier_removal — 2D `typed` op

• Data kinds: points → points

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

tb_statistical_outlier_removal: input → output

*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.*

Sweeping knob a (0.1 / 0.5 / 0.9, the other knob at its default):

▸ tb_statistical_outlier_removal: knob a sweep (docs site)

Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):

▸ tb_statistical_outlier_removal: knob b sweep (docs site)

Stages (the ops that come before → this op, left to right):

▸ tb_statistical_outlier_removal: stages (docs site)

On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):

▸ tb_statistical_outlier_removal: other inputs (docs site)

Usage

Removes points whose k-nearest-neighbor mean distance is a global outlier (statistical outlier removal).

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

点ごとに「最も近い k 個(自分自身は除く)までの平均距離」を測り、その全点分布の

`mean + std_ratio*std` を超える点を飛び点とみなして落とす。まばらな飛び点の掃除に

有効で、密な面上の点は残る。

Parameters

----------

points : array_like, shape (N, 3)

入力点群。

k : int

近傍数(既定 16)。点数が少なければ内部で `n-1` に丸める。

std_ratio : float

しきい値の緩さ。大きいほど残りやすい(除去が緩い)。

Returns

-------

filtered : ndarray, shape (M, 3)

生き残った点(元の順序を保持)。

keep_mask : ndarray of bool, shape (N,)

各入力点を残すか(True=残す)。`points[keep_mask] が filtered` に等しい。

Notes

-----

点数 < 3 では統計が立たないため、全点を残す(graceful)。

2-D 進化レジストリへ橋渡しした 3d の op `statistical_outlier_removal。実装は同じで、呼び出し規約だけ op(v, a, b) に合わせてある。a が k(既定 16)、b が std_ratio`(既定 2)を振る。

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.

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.

img_to_points 0.50 0.50
tb_statistical_outlier_removal 0.50 0.50

▸ Load this pipeline · Load & run

Runnable examples (verified samples that actually call this op)

The examples below call the underlying ledger op statistical_outlier_removal. This bridge op is the same implementation adapted to the fn(v, a, b) convention, so the behaviour carries over unchanged (only the call form differs).

• pcl_geodesic — py -3.11 examples_3d/pcl_geodesic.py

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

identity · tb_points_to_voxel · tb_estimate_point_normals · tb_iss_keypoints · tb_project_points · tb_render_point_depth · tb_radius_outlier_removal · tb_voxel_grid_downsample

Same category (`typed`)

tb_points_to_voxel · tb_estimate_point_normals · tb_iss_keypoints · tb_project_points · tb_render_point_depth · tb_radius_outlier_removal · tb_voxel_grid_downsample · tb_mls_smooth

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

tb_statistical_outlier_removal — 2D typed op