Robust Subspace Position Measurement Using Localized Sub-Windows
Bibliographic record
Abstract
The use of localized principal component analysis is examined for visual position determination in the presence of varying degrees of occlusions. Occlusions lead to substantial position measurement errors when projecting images into eigenspace. One way to improve robustness to occlusions is to select small sub-windows so that if some sub-windows are occluded, others can still accurately identify position. The location of candidate sub-windows are predetermined from a set of training images by subtracting the average image from each and then selecting regions using an attention operator. Since attention operators can be computationally time-intensive, the location of all sub-windows are determined a-priori during the training phase. The sub-windows in each of the training images are then projected into eigenspace. Once the training phase is complete, the run-time execution can be performed efficiently since all the sub-windows have been preselected. Input images are classified by each sub-window; majority voting is then used to determine the position estimate. Various experiments are performed including linear and rotational motion, and the ego motion of a mobile robot. This technique is shown to provide greater position measurement accuracy in the presence of severe occlusions as compared to the projection of entire images.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".