Distance Estimation Between Camera and Shrimp Underwater Using Euclidian Distance and Triangles Similarity Algorithm
Bibliographic record
Abstract
Camera is the main tool for monitoring shrimp underwater with a noninvasive method. Distance of the shrimp underwater with the varying camera causes the monitoring of the estimated shrimp size to be less accurate. This study provides a new solution to detect the distance of shrimp underwater with a camera using the Euclidian distance and triangle similarity algorithm. The problem raised in this study is how to measure the distance of a shrimp underwater with a camera. The method used has several stages, including using the grayscale image method, image thresholding, edge detection, detection of Region of Interest (ROI), determining the position of shrimp coordinates, calculating the length of shrimp coordinates, camera calibration using the triangle similarity algorithm, calculating the estimated distance of shrimp with the camera. The study results obtained a focal length value of 1298.58, the value of the distance between the shrimp and the camera (D') for 5 positions of shrimp underwater from 50 cm – 19.86 cm, and the RE value from 0% - 0.13%. In conclusion, this method can be used to measure the estimated distance between of moving shrimp and a camera with a low error rate.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".