Optimal positioning of multiple cameras for object recognition using Cramer-Rao lower bound
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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