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Record W2387072357

Wavelet Transform in Real-time Optical Joint Transform Correlator

2006· article· en· W2387072357 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

VenueBandaoti guangdian · 2006
Typearticle
Languageen
FieldEngineering
TopicOptical Systems and Laser Technology
Canadian institutionsL'Alliance Boviteq
Fundersnot available
KeywordsWavelet transformArtificial intelligenceJoint (building)Computer visionLinearityStationary wavelet transformWaveletSecond-generation wavelet transformComputer scienceContrast (vision)Discrete wavelet transformOptical correlatorTop-hat transformFrame (networking)Harmonic wavelet transformImage (mathematics)Pattern recognition (psychology)MathematicsImage processingFourier transformDigital image processingEngineeringElectronic engineeringTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

The multi-resolution gradient information is acquired by the wavelet transform of target image,and then is processed by using the linearity or non-linearity transfromation.So a frame of enhanced wavelet coefficient can be gained.Using image restoration technology,the target image with high contrast can be obtained.Thus the real-time optical joint transform correlator can preferably recognize the target image with low contrast under complex backgound.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score0.886

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 head0.006
GPT teacher head0.183
Teacher spread0.177 · 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