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Record W2278621937 · doi:10.1007/978-0-387-36594-7_30

A Vision-Based Approach af Bare-Hand Interface Design in Virtual Assembly

2007· book-chapter· en· W2278621937 on OpenAlexafffund
Xiaobu Yuan, Jiangnan Lu

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldComputer Science
TopicHand Gesture Recognition Systems
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRobustness (evolution)Human–computer interactionComputer scienceArtificial intelligenceEngineeringRobotComputer visionEngineering drawingSimulation

Abstract

fetched live from OpenAlex

This paper presents the continuous work on a previous project on vision-based hand interaction design. The original work developed a vision-based approach of bare-hand-based posture recognition and motion tracking. This paper further investigates its application in virtual assembly. The accuracy, and robustness of the developed approach enable product engineers to perform assembly operations while manipulating virtual objects directly in virtual environments with bare hands. The direct human involvement creates a user- defined assembly sequence, which contains the human knowledge Of mechanical assembly. By extracting the precedence relationship of machinery parts, the system becomes capable of generating alternative assembly sequences for robot reprogramming. Operation of the presented approach a illustrated and analyzed with experiments.

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.536
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.054
GPT teacher head0.284
Teacher spread0.230 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
GenreOther

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

Citations1
Published2007
Admission routes2
Has abstractyes

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