Adaptive Multiview Target Classification in Synthetic Aperture Sonar Images Using a Partially Observable Markov Decision Process
Why this work is in the frame
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Bibliographic record
Abstract
The problem of classifying targets in sonar images from multiple views is modeled as a partially observable Markov decision process (POMDP). This model allows one to adaptively determine which additional views of an object would be most beneficial in reducing the classification uncertainty. Acquiring these additional views is made possible by employing an autonomous underwater vehicle (AUV) equipped with a side-looking imaging sonar. The components of the multiview target classification POMDP are specified. The observation model for a target is specified by the degree of similarity between the image under consideration and a number of precomputed templates. The POMDP is validated using real synthetic aperture sonar (SAS) data gathered during experiments at sea carried out by the NATO Undersea Research Centre, and results show that the accuracy of the proposed method outperforms an approach using a number of predetermined view aspects. The approach provides an elegant way to fully exploit multiview information and AUV maneuverability in a methodical manner.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 it