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Record W2545553011 · doi:10.1109/have.2004.1391900

An acquisition method for interactive deformable models

2005· article· en· W2545553011 on OpenAlexaff
Jochen Lang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceComputer visionArtificial intelligenceData acquisitionComputer graphicsDeformation (meteorology)Haptic technologyRange (aeronautics)Computer graphics (images)

Abstract

fetched live from OpenAlex

This work gives a brief review on deformable modeling in computer graphics and it summarizes our work on the acquisition of deformable models for interactive visual and haptic simulation. Our method for deformable model acquisition is based on data obtained with the UBC active measurement (ACME) facility. ACME is a robotic measurement system which is capable of recording the local deformation of an object based on manipulator force sensing and the global deformation based on our visual range-flow technique. We have acquired data for the robust estimation of elastic models including extensions to large scale deformation and viscoelastic object behavior. We re-iterate our results obtained for a plush toy, a medical physical soft-tissue wrist model and a triceratops rubber toy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.005

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 head0.022
GPT teacher head0.369
Teacher spread0.347 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations2
Published2005
Admission routes1
Has abstractyes

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