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Record W2065505667 · doi:10.1108/02602280210416141

An imaging system with structured lighting for on‐line generic sensing of three‐dimensional objects

2002· article· en· W2065505667 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

VenueSensor Review · 2002
Typearticle
Languageen
FieldEngineering
TopicImage Processing Techniques and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceComputer visionScannerArtificial intelligenceFrame (networking)PixelLine (geometry)Laser scanningStructured lightRange (aeronautics)AlgorithmComputer graphics (images)LaserEngineeringOpticsMathematicsGeometry

Abstract

fetched live from OpenAlex

This paper focuses on the design of an inexpensive and accurate range scanner for automatic acquisition of a CAD model of a manufactured part by using two‐dimensional images to determine a digitized three‐dimensional shape. In the developed approach, the object is passed at a speed of 4 cm/s through a single linear laser stripe and forty continuous images are captured into the frame memory of the host computer for subsequent processing. A major problem that is encountered in the design of laser stripe scanner is the specula reflection, which can be mitigated by the developed approach. Six center‐locating algorithms are described, which are central to the developed approach. These algorithms are able to achieve sub‐pixel accuracy. The center of mass algorithm that uses three points, gives the best repeatability over the other algorithms. The center of mass algorithm that uses intensity threshold, provides the best linearity over the other algorithms.

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 score0.941
Threshold uncertainty score0.483

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 head0.025
GPT teacher head0.259
Teacher spread0.234 · 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