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Record W2034780874 · doi:10.1541/ieejias.126.25

Textile Surface Inspection by Using Translation Invariant Wavelet Shrinkage

2006· article· en· W2034780874 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

VenueIEEJ Transactions on Industry Applications · 2006
Typearticle
Languageen
FieldEngineering
TopicIndustrial Vision Systems and Defect Detection
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsWaveletShrinkageWavelet transformInvariant (physics)Spline (mechanical)Computer visionTranslation (biology)Artificial intelligenceMathematicsComputer scienceAlgorithmEngineeringStructural engineeringStatistics

Abstract

fetched live from OpenAlex

A visual inspection method of textile surfaces using the translation invariant Wavelet Shrinkage is presented. The Wavelet transform, while it can be computed efficiently by the Mallat algorithm, has the translation variance problem. To deal with this problem, we use RI-Spline wavelets which are pseudo Complex wavelets consist of a pair of a symmetric bi-orthogonal spline wavelet and an anti-symmetric bi-orthogonal spline wavelet, for textile surface inspection. In our approach, we remove the regular information which consists of the textile textures and the shading effects caused by uneven lighting from the textile surfaces to be inspected, using the translation invariant Wavelet Shrinkage realized using 2D RI-Spline wavelets. The experimental results show that our inspection method is effective for detecting tiny defects as well as global defects such as dyeing unevenness.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.906
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
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.019
GPT teacher head0.235
Teacher spread0.215 · 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