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Record W4250784646 · doi:10.1002/9781119111771.ch7

Light and Color Representation in Imaging Systems

2019· other· en· W4250784646 on OpenAlexaff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSpectral colorColor spaceColor histogramArtificial intelligenceFalse colorComputer visionColor imageColor balanceRepresentation (politics)GrayscaleMathematicsColor differenceOpticsComputer scienceColor modelPhysicsImage processingImage (mathematics)

Abstract

fetched live from OpenAlex

This chapter reveals the exact nature of the image signal value for both grayscale and color images. In the case of color images, it is shown to be a three-dimensional vector quantity. The chapter considers the representation of light and color in large fixed patches. Visible light is electromagnetic radiation with wavelengths roughly in the band from 350 to 780 nm. The color sensation perceived by a human viewer is largely determined by the power density spectrum of the light incident on the retina, specified by the spectral irradiance. A set of color coordinates serves to uniquely identify a specific color within the color space. A linear color representation is one where the color coordinates are tristimulus values with respect to a specific set of primaries. The luma-color-difference representation (and its variants) is one of the most widely used color coordinate systems in image compression and transmission.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0040.004
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.004

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 head0.005
GPT teacher head0.207
Teacher spread0.202 · 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
GenreMethods

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

Citations0
Published2019
Admission routes1
Has abstractyes

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Same topicCCD and CMOS Imaging SensorsFrench-language works237,207