Reconstructing the Plenoptic function from wireless multimedia sensor networks
Bibliographic record
Abstract
The development of low-cost image/video sensors has enabled wireless multimedia sensor networks (WMSN). Equipped with the ability to collect images/video streams from surrounding environments, many interesting applications will emerge through the use of WMSN. One of the key targets of a WMSN application is reconstructing the Plenoptic function by using collected images. However, due to the characteristics of wireless channels, this task is challenging. In this paper, a reliable transport protocol for WMSN is presented. This protocol utilizes JPEG stream semantics to distinguish the importance of different parts within the stream, and schedules the transmission based on the importance of the information. In order to investigate the performance of the proposed protocol, a novel image mosaicking algorithm is used as a sample WMSN application. The experimental results show that the proposed protocol and application can offer snapshots of the interested environment with larger field-of-view (FOV) and with little delay. The quality of the view can be improved gradually with the arrival of the data representing the higher frequency part of the images.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".