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Record W2146825728 · doi:10.1109/iccv.2001.937704

Real-time video phase-locked loops

2005· article· en· W2146825728 on OpenAlexaff
Jeffrey E. Boyd, M. Sayles

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGaitComputer visionComputer scienceArtificial intelligenceEntrainment (biomusicology)PhasorControl theory (sociology)Phase (matter)TreadmillPerceptionSimulationPhysicsRhythmAcousticsPower (physics)Psychology

Abstract

fetched live from OpenAlex

In the perception of gaits, timing is everything; specifi-cally, the relative timing of the individual motions in a gait, and when events occur periodically, as they do in a gait,then relative timing is equivalent to phase. The importance of phase in gaits appears in the medical, psychology, andcomputer vision literature. The video phase locked loop (vPLL) [3] is a novel sys-tem that perceives gaits, is sensitive to the phases of the component motions of the gait, and is model-free. vPLLsprovide a mechanism to perform two critical tasks in gait perception: frequency entrainment and phase locking [1].A vPLL can lock on oscillations in pixels that arise because of oscillatory motion. In doing so, the vPLL matches its in-ternal oscillators to the oscillations in pixel intensities, thus performing frequency entrainment. Phase locking occursas individual phased-locked loops at each pixel site lock simultaneously. The abundance of data extracted by the vPLLmakes gait recognition possible.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.902
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.303
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2005
Admission routes1
Has abstractyes

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