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Record W2379062939

Research on Vision-Based Linear Measurement of Rotation Parameters Using Orthonormal Optimization

2007· article· en· W2379062939 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMicrocomputer applications · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Measurement and Detection Methods
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceOrthonormal basisRobustness (evolution)Rotation (mathematics)Artificial intelligenceLinear equationFeature (linguistics)Translation (biology)AlgorithmSystem of linear equationsHough transformComputer visionMathematical optimizationImage (mathematics)MathematicsGeometry
DOInot available

Abstract

fetched live from OpenAlex

There are a lot of difficulties in traditional vision-based measurement methods.Equations which are always non-linear and complicated have to be solved,and features are often not easy to extract.Focused on these problems,a feature measurement algorithm based on Hough translation is discussed.Corresponding lines in different pictures are extracted first,and then linear equations are given by line features,finally the result matrices are orthonormalized and optimized.This algorithm has many advantages,the equations are much simpler,and the robustness to errors is good.Its effectiveness is revealed by later experiments and data comparisons.

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)0.002
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.249
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.097
GPT teacher head0.380
Teacher spread0.283 · 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