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Record W2049412506 · doi:10.1109/nafips.2006.365415

Suppression of Cracking in Albumen Prints using Fuzzy Filtering

2006· article· en· W2049412506 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

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicColor Science and Applications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCrackingFuzzy logicComputer scienceVisual artsLate 19th centuryPolychromeArtificial intelligenceComputer visionComputer graphics (images)ArtMaterials scienceAestheticsComposite materialPeriod (music)

Abstract

fetched live from OpenAlex

Photographs represent one of the oldest visual forms of media used to convey information. Since its inception in the mid 19th century, photographs have been used to document cultural heritage. Many of these historical photographs suffer from the effects some form of degradation, resulting in a deterioration of their visual appearance or physical structure. Albumen prints represent the principal photographic format of the 19th century, and are especially prone to innate processes such as yellowing and cracking. The latter is a mechanical process which diminishes the aesthetic qualities of these prints. In this paper we address the problem of cracking in albumen prints of the 19th century, and investigate the use of fuzzy filtering techniques to suppress cracking in albumen prints

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.187

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 head0.016
GPT teacher head0.283
Teacher spread0.267 · 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

Citations1
Published2006
Admission routes1
Has abstractyes

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