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Record W2920932716 · doi:10.1109/isscc.2019.8662326

5.5 Dual-Tap Pipelined-Code-Memory Coded-Exposure-Pixel CMOS Image Sensor for Multi-Exposure Single-Frame Computational Imaging

2019· article· en· W2920932716 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPixelComputer scienceComputer visionArtificial intelligenceImage sensorFrame (networking)Computational photographyCoding (social sciences)CMOS sensorComputer hardwareImage processingImage (mathematics)Telecommunications

Abstract

fetched live from OpenAlex

Modern computational photography applications such as 3D sensing, gesture analysis, and robotic navigation drive the growing need for programmability, or coding, of the camera exposure at the individual-pixel level. Unlike conventional cameras, which record all light incident onto a pixel, the emerging class of coded-exposure-pixel (CEP) cameras can be programmed to selectively detect only some of that light [1] or, better, sort all of the light [2, 3], depending on the pixel code. In conjunction with a concurrently coded illumination, this enables a wide range of new coded multi-exposure single-readout-frame imaging capabilities at video rates. This work demonstrates such an image sensor where multiple pixel-wise-coded exposures, or subframes, are accumulated during one video frame.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.659
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.014
GPT teacher head0.237
Teacher spread0.223 · 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

Quick stats

Citations20
Published2019
Admission routes1
Has abstractyes

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