New Methods to Project Panoramas for Practical and Aesthetic Purposes
Bibliographic record
Abstract
Recent advances in digital photomontage have simplified the creation of extreme wide-angle views from a vantage point, including the recreation of the entire sphere (we will refer to these type of images as panoramas). In order to minimize the distortion from the point of view of the viewer, panoramas have been typically presented using curved displays (such as the original panoramas, by Barker, in 1787; or several cinematographic systems, such as Circle-Vision 360, still in use), and more recently with the help of the computer (such as the QuickTime VR format). Unfortunately requiring such systems restricts their use, and little research has been done in the representation of panoramas into a flat surface. In this paper we propose the use of several geographic map projections to project a panorama into a flat surface, both for realistic purposes (where the projection can be easily accepted as a faithful representation of the original image) and for artistic purposes (where the projection is used as an artistic tool intended for the creation of an innovative interpretation of the panorama). Finally we explore the use of inclinometers and map projections to automatically project an image from a wide-angle lens (rectilinear or fisheye) into a new image that is more aesthetically pleasant. We believe the projections discussed in this paper will be useful to photographers, artists, and the designers of virtual reality environments, all of who might require the displaying of images with a wide field-of-view.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".