selfmotion op• Data kinds: table → matrix
• Call: import fullseye as fs; fs.ledger.fly_matched_filter(lattice, axis=(0.0, 0.0, 1.0), motion='rotation', depth_m=1.0) (to call the implementation directly, import flyvision; flyvision.fly_matched_filter(lattice, axis=(0.0, 0.0, 1.0), motion='rotation', depth_m=1.0); from the registry, opsflyvision.get("fly_matched_filter"))
The flow field one unit of self-motion writes on the eye — the template a wide-field neuron is matched to.
Krapp & Hengstenberg measured the local motion sensitivity of single lobula-plate tangential cells across the whole visual field and found a structured vector field, one that looks like the optic flow of a particular rotation of the fly (*Nature* 384:463, 1996). Reading self-motion out of such a cell is then a matched filter (Franz & Krapp, *Biol. Cybern.* 83:185, 2000):
correlate the measured flow against the template of the motion you are asking about. This op builds the template, for the isotropic world model — every point at the same distance — which is the case in which the rotation template is exactly the geometry and nothing is assumed about the scene:
• motion="rotation": one radian per second about the unit vector *axis* moves the viewing direction d at -axis x d, which is already tangent to the sphere. Its length is sin of the angle between the axis and the line of sight, so the template is zero on the axis itself.
• motion="translation": one metre per second along *axis*, with every point at *depth_m* metres, moves it at -(v - (v.d) d)/Z. The depth is an input, not a measurement — translation flow and distance are the same unknown and no eye can separate them from one frame pair.
lattice: a fly_hex_lattice result. axis: the rotation axis or translation direction in body coordinates (x forward, y left, z up); it is normalised, and a zero vector is refused. depth_m: the uniform distance, motion="translation" only.
Returns (n, 2) float64 — the azimuth and elevation components of the flow at each ommatidium, in radians per second, the same layout fly_flow_from_directions returns.
Ground truth: for a rotation, |f| = sin(angle(axis, d)) exactly, so it is 0 where the line of sight is along the axis and 1 where it is perpendicular; and the flow is perpendicular to both the axis and the line of sight. For a translation, |f| = sin(angle)/depth and the flow points away from the direction of travel (the focus of expansion is where the template vanishes).
Raises ValueError: a malformed *lattice*, an *axis* that is not three finite numbers or is zero-length, an unknown *motion*, and a non-positive *depth_m*.
• 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.
• poc_fly_optomotor_steering — py -3.11 examples/poc_fly_optomotor_steering.py
matrix as input)fly_lamina_filter · fly_onoff_split · fly_t4t5_field · fly_flow_from_directions · fly_egomotion_from_flow · fly_hs_readout
selfmotion)fly_egomotion_from_flow · fly_eye_merge
*Provenance: flyvision.py — FLYVISION operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.