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Texture Element Extraction via Cepstral Filtering in the Radon Domain

2002· article· en· W2023993038 on OpenAlex
Antônio César Germano Martins, Rangaraj M. Rangayyan

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

VenueIETE Journal of Research · 2002
Typearticle
Languageen
FieldComputer Science
TopicMedical Image Segmentation Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPreprocessorArtificial intelligenceWeightingPattern recognition (psychology)CepstrumRadon transformComputer scienceWaveletRadonTexture (cosmology)Filter (signal processing)Feature extractionComputer visionMathematicsAcousticsImage (mathematics)Physics

Abstract

fetched live from OpenAlex

We present a procedure based on one-dimensional cepstral filters in the Radon domain to extract texture elements or textons from images with (quasi-) periodic or ordered texture. With this approach, no assumption is required on the homogeneity of the texton. By applying the cepstral filter in the Radon domain, the difficulties associated with two-dimensional cepstral analysis and phase unwrapping are obviated. The necessity of a weighting function as a preprocessing step and details of wavelet extraction in the Radon domain are discussed. The method should facilitate structural analysis of ordered texture and the constituent textons.

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)0.005
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.847
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.099
GPT teacher head0.423
Teacher spread0.325 · 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