Measuring the Distance between the Two Vehicles Using Stereo Vision with Optical Axes Cross
Bibliographic record
Abstract
We will see smart cars on the street in future that has got ability to recognize the car, and has the ability to estimate the direction and distance from other vehicles or pedestrians and they can implement operations corresponding to the track for the navigation. In this paper by using stereo vision with the optical axis intersecting, by using two cameras, a camera has got rotation around on the Y-axis and using modern methods of image processing and focusing on the area of 1/5 m and using MATLAB software to estimate the distance between the vehicles. In this paper the whole process of stereo vision from image acquisition distortion compensation, image smoothing, stereo correspondence, and finally measure the distance is handled. The method used in this paper for calibration has got flexible more than other methods. The main advantage of the used method for calibration is its easy setup so that just by creating a calibration page anyone can use it. The results shows at day and in the laboratory conditions that has got acceptable accuracy %89/9.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".