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Record W2058701733 · doi:10.1117/12.606407

Architecture of triple sensitive color digital sensors with the integration of image fusion techniques

2005· article· en· W2058701733 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
Typearticle
Languageen
FieldEngineering
TopicInfrared Target Detection Methodologies
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPanchromatic filmComputer scienceComputer visionArtificial intelligenceMultispectral imageImage sensorImage resolutionImage fusionDigital imageBandwidth (computing)Sensor fusionFrame (networking)False colorImage processingColor imageImage (mathematics)Telecommunications

Abstract

fetched live from OpenAlex

High resolution and highly sensitive colour digital sensors are desired for many applications, including military and civilian missions. Due to the limitation of spectral bandwidth, the sensitivity of a digital colour sensor is usually three times lower than that of a digital panchromatic sensor with a spectral bandwidth of the entire visible range or a range from visible to near infrared. This paper introduces a conceptual architecture for producing a triple sensitive colour digital frame sensor. Automatic image fusion techniques are involved to integrate colour and panchromatic images to increase the sensitivity of the colour sensor. Available satellite colour and panchromatic images are tested to prove the concept. The test results demonstrate that the introduced architecture is promising for developing real triple sensitive colour digital sensors.

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 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 score0.418
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.231
Teacher spread0.220 · 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