Detection of Thick Elliptical Structures in Complex Images: A Mixture Model-Based Approach using Ellipse-Anchored Gaussian Distribution
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".