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Record W2107424980 · doi:10.1109/cvpr.1993.340988

A comparison of weighted LS methods with LS methods in 3-D motion estimation from stereo image sequences

2002· article· en· W2107424980 on OpenAlexaff
Minas E. Spetsakis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsYork University
Fundersnot available
KeywordsIndependent and identically distributed random variablesMathematicsMotion estimationMotion (physics)Noise (video)Covariance matrixGaussian noiseAlgorithmGaussianStructure from motionCovarianceArtificial intelligenceImage (mathematics)Applied mathematicsComputer scienceRandom variableStatistics

Abstract

fetched live from OpenAlex

Estimating 3-D motion parameters from three-dimensional point correspondences is considered. A minimum errors-in-variables formulation of the motion estimation problem is developed to obtain stable and accurate solutions and is connected with the ordinary least squares formulation of the problem. When covariance matrices of 3-D points are known, a closed-form matrix-weighted solution is derived for estimating the motion parameters in the presence of independent and identically distributed (i.i.d.) Gaussian noise. In a general case, a closed-form approximate solution is presented. The experimental results demonstrate that the authors' solutions are accurate and reliable in the presence of noise with different deviations.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.962
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.079
GPT teacher head0.460
Teacher spread0.381 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreMethods

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

Citations1
Published2002
Admission routes1
Has abstractyes

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