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Record W2078942744 · doi:10.1109/mmsp.2012.6343424

Wavelet-based gradient transform and its applications

2012· article· en· W2078942744 on OpenAlex
Ehsan Nezhadarya, Rabab Ward, Z. Jane Wang

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Image Fusion Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsWavelet transformStationary wavelet transformSecond-generation wavelet transformHarmonic wavelet transformLifting schemeWaveletWavelet packet decompositionDiscrete wavelet transformArtificial intelligencePattern recognition (psychology)Top-hat transformComputer scienceMathematicsDiagonalComputer visionImage (mathematics)Image processingImage textureGeometry

Abstract

fetched live from OpenAlex

A wavelet-based image gradient transform is proposed. The proposed transform, called multi-scale gradient transform (MSGT), obtains the first order derivative of an image in terms of the wavelet detail coefficients. While traditional methods estimate the image gradients at each wavelet scale in terms of the horizontal and vertical wavelet coefficients only, the proposed transform obtains the gradients in terms of the diagonal, as well as the horizontal and vertical wavelet coefficients. The proposed MSGT is designed to be invertible, non-redundant and computationally efficient. We demonstrate the potential applications of the proposed transform in texture feature extraction, multi-scale edge detection, image quality assessment and image watermarking.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.931
Threshold uncertainty score0.223

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 head0.008
GPT teacher head0.226
Teacher spread0.218 · 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

Quick stats

Citations2
Published2012
Admission routes1
Has abstractyes

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