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Record W2139744456 · doi:10.1109/icsmc.1995.538258

An introduction to panospheric imaging

2002· article· en· W2139744456 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSatellite Image Processing and Photogrammetry
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsComputer scienceComputer visionComputer graphics (images)Virtual realityArtificial intelligenceObserver (physics)Process (computing)Perspective (graphical)Orientation (vector space)PhysicsMathematics

Abstract

fetched live from OpenAlex

A general imaging technology which is able to capture and present substantially spherical fields of view has been developed, and a new discipline known as panospheric imaging (PI) has been recognised. PI is a technology which allows a substantially spherical field-of-view to be captured, digitally processed, and presented to an observer in the form of a fully immersive spherical perspective image, in both still and full motion video formats. This technology greatly simplifies the process of acquiring and presenting panoramic and immersive still images, and offers for the first time a practical technology for true panoramic and panospheric full motion video. PI has become feasible because of a number of separate advances, including the development of appropriate optics, the emergence of a general digital image remapping capability able to correct even severely distorted images, and the availability of wide angle virtual reality (VR) headsets with head orientation sensing, which provide an appropriate device for viewing such images.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.901
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)0.0010.000

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 head0.007
GPT teacher head0.207
Teacher spread0.200 · 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