A New Miniaturized Embedded Stereo-Vision System (MESVS-I)
Bibliographic record
Abstract
We have developed a fully integrated, miniaturized embedded stereo vision system (MESVS-I) which fits into a tiny package of 5 times 5 cm and consumes very low power (700 mA @ 3.3 V). The system consists of two small profile CMOS cameras, and a power efficient, dual-core embedded media processor, running at 600 MHz per core. The stereo-matching engine performs sub-sampling, rectification, pre-processing using rank transform, correlation-based matching using three levels of recursion, L/R consistency check and post-processing. We have proposed a novel and efficient post-processing algorithm that removes outliers due to low-texture regions and depth discontinuities by combining the contributions from the variance map of the rectified image, disparity map, and the variance map of the disparity map. To further enhance the performance of the system, we have implemented a two staged pipelined-processing scheme that takes advantage of the dual-core architecture of the embedded processor, thereby achieving a processing speed of around 10 fps for disparity maps.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".