tb_radius_outlier_removal — 2D `typed` op

• Data kinds: points → points

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

tb_radius_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.*

*Knob a does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*

*Knob b does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*

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

▸ tb_radius_outlier_removal: stages (docs site)

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

▸ tb_radius_outlier_removal: other inputs (docs site)

Usage

Removes points whose number of neighbors within radius radius is below min_neighbors (removal of isolated points).

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

各点を中心に半径 `radius の球を張り、その中に居る他点の数が min_neighbors`

に満たない点を「孤立した粒」として落とす。統計的手法より局所的・直接的で、

センサの実スケールが分かっているときにしきい値を決めやすい。

Parameters

----------

points : array_like, shape (N, 3)

入力点群。

radius : float

近傍とみなす球の半径(> 0)。

min_neighbors : int

残すのに必要な近傍数(自分自身は数えない、既定 8)。

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)。

Notes

-----

`radius <= 0` は ValueError。空入力は空を返す(graceful)。

2-D 進化レジストリへ橋渡しした 3d の op `radius_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 min_neighbors(既定 8)を振る。b` は未使用。

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_radius_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 radius_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_statistical_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_statistical_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_radius_outlier_removal — 2D typed op