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Record W1865957786 · doi:10.1109/robot.1999.770389

Panoramic video with predictive windows for telepresence applications

2003· article· en· W1865957786 on OpenAlex
J. Baldwin, Anup Basu, Huijuan Zhang

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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer visionComputer scienceRefresh rateArtificial intelligencePanoramaBandwidth (computing)Kalman filterField of viewTeleroboticsComputer graphics (images)Mobile robotRobot

Abstract

fetched live from OpenAlex

We describe the application of a predictive Kalman filter to the display of panoramic images. We discuss integrating a panoramic imaging system with prediction of viewing direction to create an effective telepresence system over low bandwidth links. Panoramic imaging using a reflective mirror surface offers an alternative to pan-tilt systems for obtaining a 360 degree field of view. Selecting a small window within a panoramic image allows a meaningful part of an image from a remote site to be seen at a higher refresh rate. Because of the delay in transmitting an image from a remote site, it is necessary to have additional image information available locally. This information can be used to simulate continuously flowing pictures with reduced apparent delay. Continuity in image viewing is achieved by predicting the next viewpoint of an operator and preemptively transmitting parts of an image. Experimental results are given to evaluate the proposed telepresence system.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.696
Threshold uncertainty score0.227

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)0.0000.000

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 head0.009
GPT teacher head0.262
Teacher spread0.253 · 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

Quick stats

Citations33
Published2003
Admission routes1
Has abstractyes

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