PCA Based Computation of Illumination-Invariant Space for Road Detection
Bibliographic record
Abstract
Illumination changes such as shadows significantly affect the accuracy of various road detection methods, especially for vision-based approaches with an on-board monocular camera. To efficiently consider such illumination changes, we propose a PCA based technique, PCA-II, that finds the minimum projection space from an input RGB image, and then use the space as the illumination-invariant space for road detection. Our PCA based method shows 20 times faster performance on average over the prior entropy based method, even with a higher detection accuracy. To demonstrate its wide applicability to the road detection problem, we test the invariant space with both bottomup and top-down approaches. For a bottom-up approach, we suggest a simple patch propagation method that utilizes the property of the invariant space, and show its higher accuracy over other state-of-the-art road detection methods running in a bottom-up manner. For a top-down approach, we consider the space as an additional feature to the original RGB to train convolutional neural networks. We were also able to observe robust performance improvement of using the invariant space over the original CNN based methods that do not use the space, only with a minor runtime overhead, e.g., 50 ms per image. These results demonstrate benefits of our PCA-based illuminationinvariant space computation.
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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 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.004 | 0.002 |
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".