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Record W2111333855 · doi:10.1109/icassp.1998.678051

Representation and estimation of motion using a dictionary of models

2002· article· en· W2111333855 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVideo Coding and Compression Technologies
Canadian institutionsUniversité du QuébecInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsMotion estimationQuarter-pixel motionComputer scienceMotion fieldMotion (physics)Artificial intelligenceRepresentation (politics)CodecComputer visionCoding (social sciences)Structure from motionPosition (finance)Block (permutation group theory)Mathematics

Abstract

fetched live from OpenAlex

This paper presents a novel method for representing motion information based on a dictionary of motion models and a tag image which indicates which motion model is used at any given image position. Each model is composed of low-order polynomial-based motion fields. The motion in most sequences can be adequately represented by a very small number of such motion models. We further present an efficient way of estimating and coding this representation. Comparative results are presented which indicate a performance superior to that of motion representations found in classical block-based codecs.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.686
Threshold uncertainty score0.105

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.100
GPT teacher head0.282
Teacher spread0.182 · 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