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Record W2098893560 · doi:10.1109/isbi.2008.4541298

Representation of time-varying shapes in the large deformation diffeomorphic framework

2008· article· en· W2098893560 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

Venuenot available
Typearticle
Languageen
FieldEngineering
Topic3D Shape Modeling and Analysis
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsRepresentation (politics)Vector fieldInterpolation (computer graphics)DiffeomorphismAlgorithmComputer scienceFlow (mathematics)Space timeSequence (biology)Field (mathematics)Computer visionArtificial intelligenceMathematicsImage (mathematics)GeometryMathematical analysis

Abstract

fetched live from OpenAlex

Tracking and representation of shape change over time is of great interest in the field of computational anatomy. We propose a longitudinal growth model which estimates the diffeomorphic flow of a baseline image passing through a series of time-points that are the observed evolution of the template over time. We optimize the full space-time flow for the sequence of images, providing a linear space representation of the shape-change via a time-dependent velocity vector field, thus application of linear techniques becomes straightforward. We test our longitudinal growth model on both synthetic and real data-sets and demonstrate flexibility in time- point spacing, generation of average growth, and robust interpolation of missing time-points.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.142

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 head0.020
GPT teacher head0.236
Teacher spread0.217 · 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

Quick stats

Citations35
Published2008
Admission routes1
Has abstractyes

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