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Record W1875122754 · doi:10.1109/wqv.1993.262947

Motion understanding from qualitative visual dynamics

2002· article· en· W1875122754 on OpenAlexaff
E. Shavit, Allan Jepson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicArtificial Intelligence in Games
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRepresentation (politics)Motion (physics)Function (biology)Computer scienceDynamics (music)Expression (computer science)Artificial intelligenceComponent (thermodynamics)DepictionPhysics

Abstract

fetched live from OpenAlex

In many situations, motion is naturally grasped as an ordered sequence of poses, expressing stages in a general development. Capturing the underlying dynamics is an important first step for gaining insight and constructing a representation that has expressive power. The authors compute a pose function for a motion that qualitatively characterizes it in terms of dynamics. The study is based in dynamic systems analysis, constructing the function in phase space-a geometric depiction of the observed behaviour. The analysis itself is geometrical, but yields a representation of the visual dynamics. The global component of the pose function is an expression of external forces, and the local component and expression of deformation. The computation is simple, making only few general assumptions. The function captures all the relevant parameters of the motion in the sense that a qualitative simulation of the behavior is made possible, thus, facilitating subsequent reasoning about observed events.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.957
Threshold uncertainty score0.747

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)0.0000.001

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 head0.174
GPT teacher head0.369
Teacher spread0.195 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations4
Published2002
Admission routes1
Has abstractyes

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