Optimal positioning of multiple cameras for object recognition using Cramer-Rao lower bound
Bibliographic record
Abstract
In this paper the problem of active object recognition/pose estimation is investigated. The principle component analysis is used to produce an observation vector from images captured simultaneously by multiple cameras from different view angles of an object belonging to a set of a priori known objects. Models of occlusion and sensor noise have been incorporated into a probabilistic model of sensor/object to increase the robustness of the recognition process with respect to such uncertainties. A recursive Bayesian state estimation problem is formulated to identify the object and estimate its pose by fusing the information obtained from the cameras at multiple steps. In order to enhance the quality of the estimates and to reduce the number of images taken, the positions of the cameras are controlled based on a statistical performance criterion, the Cramer-Rao lower bound (CRLB). Comparative Monte Carlo experiments conducted with a two-camera system demonstrate that the features of the proposed method, i.e. information fusion from multiple sources, active optimal sensor planing, and occlusion modelling are all highly effective for object classification/pose estimation in the presence of structured noise
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".