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Record W4319599285 · doi:10.2352/cic.2022.30.1.23

A 360° Omnidirectional Photometer using a Ricoh Theta Z1

2022· article· en· W4319599285 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

VenueColor and Imaging Conference · 2022
Typearticle
Languageen
FieldComputer Science
TopicImage Enhancement Techniques
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence Fund
KeywordsLuminancePhotometerOmnidirectional antennaComputer visionComputer scienceCalibrationOpticsArtificial intelligenceComputer graphics (images)MetreRemote sensingPhysicsGeographyTelecommunications

Abstract

fetched live from OpenAlex

Spot photometers measure the luminance that is emitted or reflected from a small surface area in a physical environment. Because the measurement is limited to a “spot,” capturing dense luminance readings for an entire environment is impractical. In this paper, we provide preliminary results demonstrating the potential of using an off-the-shelf commercial camera to operate as a 360° luminance meter. Our method uses the Ricoh Theta Z1 camera, which provides a full 360° omnidirectional field of view and an API to access the camera’s minimally processed RAW images. Working from the RAW images, we describe a calibration method to map the RAW images under different exposures and ISO settings to luminance values. By combining the calibrated sensor with multi-exposure high-dynamic-range imaging, we provide a cost-effective mechanism to capture dense luminance maps of environments. Our results show that our luminance meter performs well when validated against a significantly more expensive spot photometer.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.787
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.001
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 head0.038
GPT teacher head0.280
Teacher spread0.242 · 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