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Record W2095763972 · doi:10.1109/ccece.2008.4564754

The nonredundant contourlet transform (NRCT): A multiresolution and multidirection image representation

2008· article· en· W2095763972 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueConference proceedings - Canadian Conference on Electrical and Computer Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced Image Fusion Techniques
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsContourletWavelet transformArtificial intelligenceRedundancy (engineering)MathematicsComputer visionFilter bankPattern recognition (psychology)WaveletComputer scienceFilter (signal processing)

Abstract

fetched live from OpenAlex

As a directional multiresolution image representation, the contourlet transform can efficiently capture curved and oriented geometrical structures in images. However, it has the drawback of a 4/3 redundancy in its oversampling ratio. This paper proposes an innovative framework of filter banks to achieve the same frequency partition as the contourlet transform, while eliminating its redundancy. The new image representation is called the nonredundant contourlet transform (NRCT). The proposed NRCT possesses two important properties: critical sampling and perfect reconstruction. Moreover, the NRCT is an extension of the wavelet transform. Numerical experiments are presented to demonstrate the potential of the NRCT for image compression.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score1.000

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 head0.012
GPT teacher head0.206
Teacher spread0.194 · 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