Simple texture descriptors for classifying monochrome planetary rover terrains
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Planetary rovers face mobility hazards associated with various classes of terrains they traverse: sand, bedrock, and rock-strewn terrain. This work develops visual classifiers for these 3 terrain types for single monochrome navigation images from the NASA Mars Exploration Rover missions. The classifiers are based primarily on visual texture, captured in histograms of edges filter responses at various scales and orientations. Monochrome image intensity is further used to distinguish between confusing rock and bedrock cases. Three approaches are investigated: a gradient-based simplified HOG descriptor, a simplified GIST descriptor, and MR8 textons. Local rotational invariance is implemented in each approach, as validation tests demonstrate its benefit to performance. K-Nearest Neighbors is used for the final classification. No major differences in performance are observed between the three approaches, leading to the adoption of the HOG approach due to its lower computational complexity and thus highest applicability to planetary missions. Final tests demonstrate an accuracy between 70% and 93% (81% average) for the 3-way classification using the simplified HOG descriptor.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it