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Record W1469842922 · doi:10.1117/12.2179591

Single-plane versus three-plane methods for relative range error evaluation of medium-range 3D imaging systems

2015· article· en· W1469842922 on OpenAlexaff
David MacKinnon, Luc Cournoyer, J.‐A. Beraldin

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2015
Typearticle
Languageen
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsRange (aeronautics)Plane (geometry)Context (archaeology)OpticsApproximation errorMetric (unit)Image planeComputer scienceComputer visionAlgorithmGeometryPhysicsMathematicsMaterials scienceEngineeringImage (mathematics)

Abstract

fetched live from OpenAlex

Within the context of the ASTM E57 working group WK12373, we compare the two methods that had been initially proposed for calculating the relative range error of medium-range (2 m to 150 m) optical non-contact 3D imaging systems: the first is based on a single plane (single-plane assembly) and the second on an assembly of three mutually non-orthogonal planes (three-plane assembly). Both methods are evaluated for their utility in generating a metric to quantify the relative range error of medium-range optical non-contact 3D imaging systems. We conclude that the three-plane assembly is comparable to the single-plane assembly with regard to quantification of relative range error while eliminating the requirement to isolate the edges of the target plate face.

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)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.085
GPT teacher head0.323
Teacher spread0.238 · 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 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

Citations9
Published2015
Admission routes1
Has abstractyes

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