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Record W2015192521 · doi:10.1145/508791.508939

A mesh update requirement for hierarchical adaptive meshes in mesh-based motion tracking

2002· article· en· W2015192521 on OpenAlex
Alfred C. H. Yu, Wael Badawy

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 institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsPolygon meshComputer scienceBenchmark (surveying)Frame (networking)Tracking (education)Mesh generationMotion (physics)Volume meshTriangle meshTopology (electrical circuits)T-verticesComputer visionComputer graphics (images)Finite element methodMathematicsEngineering

Abstract

fetched live from OpenAlex

This paper presents a mesh update requirement for hierarchical adaptive meshes in mesh-based motion tracking. The requirement states that since hierarchical adaptive meshes are constructed according to the video contents of a predicted frame, constructing a different mesh topology for each predicted frame is necessary in order to most accurately describe the video contents in a predicted frame. It has been statistically verified in the analysis section of this paper that if the requirement is not satisfied, the prediction quality would be lowered. The analysis is performed on different QCIF benchmark sequences.

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: Methods · Consensus signal: none
Teacher disagreement score0.913
Threshold uncertainty score0.482

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 science0.0010.000
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 head0.079
GPT teacher head0.277
Teacher spread0.198 · 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