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Record W2157468699 · doi:10.1109/tcsvt.2003.819177

Multiplicationless Burt and Adelson's Pyramids for Motion Estimation

2004· article· en· W2157468699 on OpenAlexaff
J. Zan, M. Omair Ahmad, M.N.S. Swamy

Bibliographic record

VenueIEEE Transactions on Circuits and Systems for Video Technology · 2004
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsConcordia University
Fundersnot available
KeywordsPyramid (geometry)Motion estimationKernel (algebra)Computer scienceArtificial intelligenceComputer visionMotion (physics)Quarter-pixel motionPoint (geometry)MathematicsAlgorithmDiscrete mathematicsGeometry

Abstract

fetched live from OpenAlex

It is shown that, by choosing appropriate values for the parameter a of the generating kernel in constructing P.J. Burt and E.H. Adelson's pyramid for motion estimation (see IEEE Trans. Commun., vol.31, p.337-45, 1983), one can eliminate the operation of the floating point multiplications needed in such a construction and reduce the computational load to the same order as in the case of the mean pyramid. When a is chosen to be 3/8, it is demonstrated, through simulation studies, that the corresponding Burt and Adelson's pyramid does not degrade the performance of motion estimation, as compared to that using kernels giving the best performance, and this pyramid also provides perceptually better motion-compensated images than those provided by the mean pyramid.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.541

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 head0.022
GPT teacher head0.280
Teacher spread0.258 · 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 designSimulation or modeling
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

Citations3
Published2004
Admission routes1
Has abstractyes

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