Computer Vision-Based Identification of Individual Turtles Using Characteristic Patterns of Their Plastrons
Bibliographic record
Abstract
The identification of pond turtles is important to scientists who monitor local populations, as it allows them to track the growth and health of subjects over their lifetime. Traditional non-invasive methods for turtle recognition involve the visual inspection of distinctive coloured patterns on their plastron. This visual inspection is time consuming and difficult to scale with a potential growth in the surveyed population. We propose an algorithm for automatic identification of individual turtles based on images of their plastron. Our approach uses a combination of image processing and neural networks. We perform a convexity-concavity analysis of the contours on the plastron. The output of this analysis is combined with additional region-based measurements to compute feature vectors that characterize individual turtles. These features are used to train a neural network. Our goal is to create a neural network which is able to query a database of images of turtles of known identity with an image of an unknown turtle, and which outputs the unknown turtle's identity. The paper provides a thorough experimental evaluation of the proposed approach. Results are promising and point towards future work in the area of standardized image acquisition and image denoising.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".