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Record W2062904527 · doi:10.1142/s0219467804001506

INTER-FRAME PREDICTION OF MEDICAL AND VIDEOPHONE SEQUENCES: A DEFORMABLE TRIANGLE-BASED APPROACH

2004· article· en· W2062904527 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.

Bibliographic record

VenueInternational Journal of Image and Graphics · 2004
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsInter framePolygon meshTriangle meshMotion compensationComputer visionQuadtreeArtificial intelligenceAffine transformationComputer scienceCoding (social sciences)Motion estimationMathematicsAlgorithmFrame (networking)Computer graphics (images)Reference frameGeometry

Abstract

fetched live from OpenAlex

Motion compensation using deformable triangle patches has been successfully used for low bit rate coding of videophone sequences. They were also shown to be particularly efficient for interframe coding of MRI sequences, for which the difference between image slices can be well modeled by locally affine deformations. Regular triangular meshes were used in previous works. In this paper, we present a quadtree decomposition algorithm to generate a triangle mesh for which smaller triangles are used in image areas where the motion or deformation is more complex. Grid points are recursively added to areas where the reduction in prediction error is more significant. Results show that using variable size triangular patches increases the PSNR of the motion-compensated image while reducing the number of grid points when compared to a regular triangular mesh.

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)0.001
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 score0.835
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.016
GPT teacher head0.283
Teacher spread0.267 · 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