An Egg Volume Measurement System Based on the Microsoft Kinect
Why this work is in the frame
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Bibliographic record
Abstract
Measuring the volume of bird eggs is a very important task for the poultry industry and ornithological research due to the high revenue generated by the industry. In this paper, we describe a prototype of a new metrological system comprising a 3D range camera, Microsoft Kinect (Version 2) and a point cloud post-processing algorithm for the estimation of the egg volume. The system calculates the egg volume directly from the egg shape parameters estimated from the least-squares method in which the point clouds of eggs captured by the Kinect are fitted to novel geometric models of an egg in a 3D space. Using the models, the shape parameters of an egg are estimated along with the egg's position and orientation simultaneously under the least-squares criterion. Four sets of experiments were performed to verify the functionality and the performance of the system, while volumes estimated from the conventional water displacement method and the point cloud captured by a survey-grade laser scanner serve as references. The results suggest that the method is straightforward, feasible and reliable with an average egg volume estimation accuracy 93.3% when compared to the reference volumes. As a prototype, the software part of the system was implemented in a post-processing mode. However, as the proposed processing techniques is computationally efficient, the prototype can be readily transformed into a real-time egg volume system.
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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 | 0.001 | 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