Bibliographic record
Abstract
This thesis introduces a novel local appearance method, termed coherency filtering, which allows for the robust detection and analysis of rigid objects contained in heterogeneous scenes by properly exploiting the wealth of information returned by a k-nearest neighbours (k-NN) classifier. A significant advantage of k-NN classifiers is their ability to indicate uncertainty in the classification of a local window by returning a list of k candidate classifications. Classification of a local window can be inherently uncertain when considered in isolation since local windows from different objects or the background may be similar in appearance. In order to robustly identify objects in a query image, a process is needed to appropriately resolve this uncertainty. Coherency filtering resolves this uncertainty by imposing constraints across the colour channels of a query image along with spatial constraints between neighbouring local windows in a manner that produces reliable classification of local windows and ultimately results in the robust identification of objects. Extensive experimental results demonstrate that the proposed system can robustly identify objects contained in test images focusing on pose, scale, illumination, occlusion, and image noise. A qualitative comparison with four state-of-the-art systems indicates comparable or superior performance on test sets of similar difficulty can be achieved by the proposed system, while being capable of robustly identifying objects under a greater range of viewing conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".