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Color in Complex Scenes

2007· review· en· W2165267220 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

VenueAnnual Review of Psychology · 2007
Typereview
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
FundersNational Eye Institute
KeywordsHueChromatic scaleComputer visionArtificial intelligenceBrightnessObject (grammar)Color visionPerceptionOrientation (vector space)Representation (politics)Color spaceContext (archaeology)CommunicationPsychologyComputer scienceMathematicsOpticsPhysicsGeographyGeometryImage (mathematics)

Abstract

fetched live from OpenAlex

The appearance of an object or surface depends strongly on the light from other objects and surfaces in view. This review focuses on color in complex scenes, which have regions of different colors in view simultaneously and/or successively, as in natural viewing. Two fundamental properties distinguish the chromatic representation evoked by a complex scene from the representation for an isolated patch of light. First, in complex scenes, the color of an object is not fully determined by the light from that object reaching the eye. Second, the chromatic representation of a complex scene contributes not only to hue, saturation, and brightness, but also to other percepts such as shape, texture, and object segmentation. These two properties are cornerstones of this review, which examines color perception with context that varies over space or time, including color constancy, and chromatic contributions to such percepts as orientation, contour, depth, and motion.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.928
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.340
GPT teacher head0.557
Teacher spread0.217 · 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