running_mean_std — VIDEOSTREAM `recursive` op

• Data kinds: video → table

• Call: import fullseye as fs; fs.ledger.running_mean_std(video) -> 'dict' (to call the implementation directly, import videostream; videostream.running_mean_std(video) -> 'dict'; from the registry, opsvideostream.get("running_mean_std"))

Usage

Welford per-pixel mean / population std over the clip → {"mean", "std", "n"} (table).

Equals video.mean(0) / video.std(0) but needs two images of state, not the clip — the streaming form for a recording that never ends.

Detailed usage guide

• video_streaming family guide

Background guides (the physics and conventions behind this op)

• mv_cables — Cables (standards, speed, power delivery, robot cables)

• mv_frame_grabbers — Frame grabber boards (not optics, but they decide whether you can capture at all)

• mv_standards — Camera interface standards and the bodies behind them

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)

• video_streaming — py -3.11 examples/video_streaming.py

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

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Same category (`recursive`)

frame_difference_causal · exponential_background · exponential_foreground

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

running_mean_std — VIDEOSTREAM recursive op