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Record W2039272940 · doi:10.1117/12.467719

Shape measurement using an experimental untracked range sensor

2002· article· en· W2039272940 on OpenAlexafffund
George K. Knopf, Archana Sangole, Jonathan Kofman

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2002
Typearticle
Languageen
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsUniversity of OttawaWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer visionComputer scienceOpticsStructured-light 3D scannerCoordinate systemProjectorArtificial intelligenceImage sensorStructured lightPhysicsScanner

Abstract

fetched live from OpenAlex

An experimental shape measurement system that does not require peripheral sensors to track the position and orientation of the sensor-head is described in this paper. The prototype consists principally of a multiple-line light projector and a CCD camera. The light projection unit uses a low-power diode laser with a single line-generator and several dichroic cube beamsplitters. This simple hardware configuration creates three parallel line profiles with unique intensity values due to the transmission and reflection properties of the constituent beamsplitters. To eliminate background information, a bandpass filter with a peak response near the wavelength of the laser source is placed over the camera lens. The CCD camera acquires images of distorted light patterns as the sensor-head is swept across the object surface. The coordinate points of the parallel profiles in each view are recovered relative to the sensor head using a nonlinear image-to-object coordinate calibration technique. Where partial overlap exists between adjacent views, a multi-view registration algorithm can be applied. In the proposed registration method, the geometry of the surface in each view is approximated prior to computation of the sensor transformation. These synthetic surfaces are used to establish the corresponding features in adjacent views that are necessary to compute the translation and rotation parameters of the sensor-head. The potential of the surface-measurement method is demonstrated using narrow overlapping views.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score1.000

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 communication0.0000.002
Open science0.0020.000
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 head0.057
GPT teacher head0.260
Teacher spread0.204 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2002
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicOptical measurement and interference techniquesFrench-language works237,207