MétaCan
Menu
Back to cohort
Record W2534574868 · doi:10.1109/icics.2005.1688997

A Hierarchical Algorithm for Fast Projective Flow Estimation

2006· article· en· W2534574868 on OpenAlexaff
Hsin-Pin Lo, Steve Mann

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsZoomComputer scienceRotation (mathematics)AlgorithmTranslation (biology)Projective testMotion estimationSequence (biology)Inter frameCoordinate systemOptical flowArtificial intelligenceTransformation (genetics)Computer visionFrame (networking)MathematicsReference frameImage (mathematics)

Abstract

fetched live from OpenAlex

This paper proposes a two-stage featureless hierarchical algorithm to alleviate the problems encountered in the VideoOrbits (and other related) algorithms for estimation of the projective coordinate transformation between images. Translation, rotation and zoom parameters are estimated by phase correlation methods in the first stage, which are used as initial estimates of Orbits in the second stage. In particular, a hybrid algorithm devised to use the first stage only when the projective motion is large demonstrates the most significant improvements in efficiency and accuracy over using Orbits alone on a motion sequence with large interframe movements.

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)0.000
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: Methods
Teacher disagreement score0.982
Threshold uncertainty score0.202

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 communication0.0000.000
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 head0.010
GPT teacher head0.283
Teacher spread0.273 · 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
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Vision and ImagingFrench-language works237,207