Bibliographic record
Abstract
Multiple off-the-shelf cameras can be configured to simultaneously provide redundant data, complementary information, and fast processing through sensor parallelism. The redundancy in the captured data can increase the accuracy of scene interpretation and improve system reliability by reducing the overall uncertainty associated with feature classification. Complementary information extracted from several cameras allows novel features in the environment to be identified that are normally impossible to detect with an individual CCD camera or range scanner. An unsolved problem in using multiple cameras for part identification or fault detection is associating the image features captured by one camera with that from another camera, or the same camera at a different point in time. In this paper, a spherical self-organizing feature map (SOFM) is used to combine and correlate both redundant and complementary features extracted from the images acquired by a multiple camera system. An important feature of the proposed technique is that the spherical SOFM develops a topologically ordered representation of the feature vectors derived from a high-dimensional input space. The unsupervised learning algorithm exploits hidden redundancies in the data set and ensures that 'similar' feature vectors will be assigned to cluster units that lie in identifiable neighborhoods on the spherical lattice. To illustrate the proposed methodology, a spherical SOFM that classifies the feature vectors acquired by a trinocular camera system is described.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".