MétaCan
Menu
Back to cohort
Record W2606250695 · doi:10.11159/icte17.111

A Study on Developing Image Processing for Smart Traffic Supporting System Based on AR

2017· article· en· W2606250695 on OpenAlexvenueno aff
Byeong-Wook Nam, Kyungho Lee, Jung Min Lee

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2017
Typearticle
Languageen
FieldComputer Science
TopicVisual Attention and Saliency Detection
Canadian institutionsnot available
FundersMinistry of Trade, Industry and Energy
KeywordsComputer scienceImage processingImage (mathematics)Computer vision

Abstract

fetched live from OpenAlex

This paper suggests a smart traffic supporting system on the vessel based on augmented reality technology. International Hydrographic Organization(IHO) adopted standard S-100 as the international standard Geographic Information System(GIS) that can be generally used in the maritime sector. The smart traffic supporting system means system that obtains the information of the real world through a camera and visualizes traffic data on the image based on S-100. At this time, there is a need for a variety of image processing techniques in order to obtain a reliable information from the image obtained through the camera. In this study, we performed the image processing that can process an image noise which is generated by the operating environment and can detect an object in order to support safe traffic. finally, we verified the technology by developing a prototype.

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 categoriesnone
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 score0.748
Threshold uncertainty score0.724

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 studies0.0010.000
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 head0.013
GPT teacher head0.248
Teacher spread0.234 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Civil, Structural, and Environmental EngineeringSame topicVisual Attention and Saliency DetectionFrench-language works237,207