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Record W2538047945 · doi:10.1109/tic-sth.2009.5444442

Image processing for colour blindness correction

2009· article· en· W2538047945 on OpenAlexaff
S Poret, R.D. Dony, Stefano Gregori

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicColor Science and Applications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBlindnessComputer visionArtificial intelligenceColour VisionFilter (signal processing)Computer scienceOptometryMathematicsMedicine

Abstract

fetched live from OpenAlex

Colour blindness is a genetic mutation that alters the colour vision of the subjects by decreasing the sensitivity to certain colour wavelengths, depending on the defect. There are many forms of colour blindness ranging from monochromacy (black-white) to the most common form, the ¿red-green¿ variation where reds or greens are weakened, the vibrant shades are easily seen and the dull shades are difficult to perceive. A filter was designed based on the Ishihara colour tests in order to correct the colour blind deficiencies. This was successful for seeing the hidden objects within the test plates but did not translate well for real world images. The filter was modified, removing the dullest/lightest shades and shifting all the shades to the darker vibrant shades. The original image was shown to colour blind and normal vision subjects with results varying among all the subjects. After the modified filter was applied to a natural image, the colour blind and normal vision subjects were all able to correctly identify the test colours.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.008

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 head0.013
GPT teacher head0.305
Teacher spread0.292 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations44
Published2009
Admission routes1
Has abstractyes

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