Detection of Thick Elliptical Structures in Complex Images: A Mixture Model-Based Approach using Ellipse-Anchored Gaussian Distribution
Why this work is in the frame
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Bibliographic record
Abstract
The detection of elliptical shapes in images plays a vital role in numerous computer vision applications, including medical imaging, industrial inspection and remote sensing. While traditional methods primarily focus on thin, well-defined elliptical contours using edge detection or curve fitting, real-world images often present thick, blurred, or partially occluded elliptical structures that challenge these techniques. In this work, we tackle the problem of detecting multiple thick elliptical regions by introducing a novel probabilistic approach based on Finite Mixture Models, where a new distribution, called Ellipse-Anchored Gaussian Distribution (EllAGD) is defined and which models the intensity distribution around elliptical shapes and incorporates a spatial thickness parameter that effectively captures thick contours. An Expectation-Maximization algorithm is used to estimate all the multiple ellipse parameters including spatial occupancy rate, center, axes, orientation angle and thickness. The proposed method is applied on synthetic and real images containing single or multiple thick elliptical structures where, it shows its ability to recover accurately all the structures parameters even with challenging image conditions.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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