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Color in Complex Scenes

2007· review· en· W2165267220 on OpenAlexaff
Steven K. Shevell, Frederick A. A. Kingdom

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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

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 designNot applicable
Domainnot available
GenreReview

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

Citations224
Published2007
Admission routes1
Has abstractyes

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