Multi-polarimetric textural distinctiveness for outdoor robotic saliency detection
Bibliographic record
Abstract
Mobile robots that rely on vision, for navigation and object detection, use saliency approaches to identify a set of potential candidates to recognize. The state of the art in saliency detection for mobile robotics often rely upon visible light imaging, using conventional camera setups, to distinguish an object against its surroundings based on factors such as feature compactness, heterogeneity and/or homogeneity. We are demonstrating a novel multi- polarimetric saliency detection approach which uses multiple measured polarization states of a scene. We leverage the light-material interaction known as Fresnel reflections to extract rotationally invariant multi-polarimetric textural representations to then train a high dimensional sparse texture model. The multi-polarimetric textural distinctiveness is characterized using a conditional probability framework based on the sparse texture model which is then used to determine the saliency at each pixel of the scene. It was observed that through the inclusion of additional polarized states into the saliency analysis, we were able to compute noticeably improved saliency maps in scenes where objects are difficult to distinguish from their background due to color intensity similarities between the object and its surroundings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".