Real‐time depth‐mapping three‐dimension TV camera (Axi‐Vision camera)
Bibliographic record
Abstract
Abstract A three‐dimension camera (Axi‐Vision camera) has been developed that is capable of producing a color image of an object and performing high‐speed mapping of depth information from the camera to the object. With this camera, intensity‐modulated near‐infrared light is irradiated onto the object, and distances to points on the object are computed from the image captured by a camera that has a high‐speed shutter. Since a two‐dimensional scanning mechanism for a laser beam or complex computational processing is unnecessary in this depth mapping system, the distance to the object can be detected at high speed for each pixel of a TV image. For the present paper, a three‐dimension camera adapted to a standard television signal was constructed by development of a high‐output light‐emitting‐diode (LED) array light source capable of high‐speed intensity modulation, an image intensifier capable of high‐speed shutter operation on the nanosecond order, and an optical system. The prototype camera can output a depth image with a resolution of 768 × 493 pixels at a frame rate of 15 Hz, the characteristic depth mapping resolution of the camera is 1.8 cm (when the distance from the camera to the object is 2 m), and the camera can capture an image of a person‐sized object. It was also shown that the depth information thus obtained could be applied to live‐action filming and to CG and other new types of image synthesis. © 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 37(8): 77–89, 2006; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/scj.20320
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".