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Record W2065735148 · doi:10.1117/12.503877

Extending the focal depth of an optical tracking system using a phase pupil plate

2003· article· en· W2065735148 on OpenAlexaff
Xinping Liu, Xianyang Cai, Chander P. Grover

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2003
Typearticle
Languageen
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsPupil functionPupilOpticsPoint spread functionExit pupilCardinal pointFocal pointGaussianPhase (matter)Entrance pupilFocal lengthDepth of fieldComputer sciencePhysics

Abstract

fetched live from OpenAlex

In this paper we present a normalized method of deriving a phase pupil function to extend the focal depth of imaging systems specially used for small object tracking. The method is based on the concept that the intensity distribution in the vicinity of the focal plane can be controlled and redistributed by means of a phase pupil function. This phase pupil function allows the peak intensity of a point-spread function (PSF) of the imaging system to remain relatively uniform, and the profile of the PSF to be approximately fitted to a Gaussian function in an extended range of the focal depth. A rotational symmetric aspheric phase plate has been designed and fabricated. The imaging system incorporating this pupil plate has extended the focal depth more than twofold compared with a conventional imaging system. Theoretical analyses and experimental results are also presented in support of this method.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.037
GPT teacher head0.278
Teacher spread0.241 · 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 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

Citations0
Published2003
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicOptical measurement and interference techniquesFrench-language works237,207