Progressive image mosaicking in wireless image sensor networks
Bibliographic record
Abstract
The prevailing image mosaicking algorithms are based on a collection of full images that are captured by camera sensors. However, these approaches cannot be directly applied to the emerging wireless image sensor networks (WISNs). Wireless channel insert noticeable delay before an entire image can be transmitted to the sink node in a WISN. In this paper, we propose a Progressive Image Mosaicking Algorithm (PIMA) based on the multi-scans feature of Progressive JPEG. PIMA's most distinguishing feature is that it accomplishes image mosaicking by using portions of images of a proper quality level to deliver an approximate view of the scene in a short time during the reception of the image data stream. Thereafter, it amends the image registration on the other two finer levels to gradually enhance the display quality. A variation of Sum of Absolute Difference (SAD) is used to improve the accuracy of image registration. Experimental results show that PIMA successfully decreases the delay for displaying the first scene, and preserves an equivalent performance to the existing patch-based image mosaicking algorithms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".