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Record W2588430516 · doi:10.1109/dasip.2016.7853808

Session 5: Image processing on multicore plateforms

2016· article· en· W2588430516 on OpenAlexaff
Pierre Langlois

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptical Polarization and Ellipsometry
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsComputer scienceSession (web analytics)Image processingHistogramMulti-core processorMassively parallelParallel computingVariety (cybernetics)Field-programmable gate arrayComputer architectureParallel processingCUDAImage (mathematics)Artificial intelligenceEmbedded system

Abstract

fetched live from OpenAlex

This session proposes four papers, that, although all related to image processing, offer a great variety of algorithms, application domains and implementation targets. The algorithms include matrix processing, optical flow, the computation of image features with the Histogram of Oriented Gradients and hyperspectral imaging. The implementation targets include Intel processors, ASIPs, FPGAs and a Massively Parallel Processor Array. The session can thus appeal to all researchers interested in the implementation of computationally intensive image processing algorithms on a variety of platforms.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.009
GPT teacher head0.227
Teacher spread0.218 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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