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Record W4243718635 · doi:10.1109/icpr.2004.1333719

Camera self-calibration: a new approach for solving the modulus constraint

2004· article· en· W4243718635 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

VenueProceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. · 2004
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCoplanarityConstraint (computer-aided design)Quintic functionConstant (computer programming)Nonlinear systemMathematicsSet (abstract data type)CalibrationInfinityMathematical analysisScale (ratio)Plane (geometry)Applied mathematicsComputer scienceGeometryPhysics

Abstract

fetched live from OpenAlex

In this paper, the modulus constraint is used to retrieve the scale factors that are responsible for the nonlinearity of Kruppa's equations. In the case of constant intrinsic parameters, each pair of images identifies a pair of 3D points at infinity whose coordinates are expressed in terms of the scale factors we are looking for. By enforcing the coplanarity constraint on these points, a set of quintic bivariate equations is obtained for each triplet of images. Once the scale factors are calculated, the problems of retrieving the plane at infinity and solving Kruppa's equations become straightforward and linear.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.887
Threshold uncertainty score0.752

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 communication0.0000.001
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 head0.052
GPT teacher head0.284
Teacher spread0.232 · 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