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Specular Streaks in Stereo

2022· article· en· W4312270429 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

Venue2022 26th International Conference on Pattern Recognition (ICPR) · 2022
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
Fundersnot available
KeywordsSpecular reflectionSpecular highlightOblique caseGeologyComputer visionComputer scienceObserver (physics)Light sourceVertical planeArtificial intelligenceOpticsComputer graphics (images)Physics

Abstract

fetched live from OpenAlex

When a compact light source illuminates a horizontal shiny ground plane at an oblique angle, the resulting highlight is vertically oriented and highly elongated. We refer to such highlights as specular streaks. Specular streaks occur commonly on wet roadways, especially at night, for example the reflections of street lamps or car headlights. Here we present a 3D model of specular streaks seen by a binocular observer. We show that specular streaks produce binocular disparities that are consistent with 3D near-vertical columns of light beneath the roadway. We validate this model with results on both synthetic and real images. Specular streaks are an important problem for vision-in-bad-weather, in particular, for autonomous driving systems or driver assistance systems that rely on stereo to estimate scene depth.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.675
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0650.001

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 head0.169
GPT teacher head0.347
Teacher spread0.178 · 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