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Record W2013953231 · doi:10.1117/12.526929

Biomimetic sampling architectures for CMOS image sensors

2004· article· en· W2013953231 on OpenAlexafffund
Fayçal Saffih, Richard Hornsey

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsYork UniversityUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsPyramid (geometry)Image sensorPixelSampling (signal processing)Computer scienceDiagonalCMOSBlock (permutation group theory)Image resolutionCorrelated double samplingCMOS sensorArtificial intelligenceArchitectureComputer visionElectronic engineeringEngineeringOpticsPhysicsMathematicsGeometry

Abstract

fetched live from OpenAlex

We demonstrate a non-orthogonal architecture for a CMOS active pixel image sensor, called here pyramid architecture, for improved two-dimensional spatial sampling. In the pyramid architecture 2D sampling using concentric rings replaces the 1D row sampling in the classical imager architecture, and diagonal output busses replace the conventional vertical column busses. Moreover, we propose a scanning scheme in which, instead of rolling over to the first ring (or row) at the end of image capture, the scan returns from the outer ring towards the first inner ring at the centre of the sensor. This leads to two scenes of differing integration times that, after being fused, results in a foveated increase in intra-scene dynamic range. Results from a sensor fabricated in 0.18μm CMOS technology are presented and discussed. We will also present a multi-resolution architecture which is based on the pixel structures as building block to control the acquired image resolution.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.314
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.232
Teacher spread0.221 · 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 teacher head, not a consensus.

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

Citations4
Published2004
Admission routes2
Has abstractyes

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