Extraction of shape elements in low-level processing for image understanding systems
Bibliographic record
Abstract
Extraction of shape elements from images is one of the most important tasks in scene analysis systems. In the approach presented in this paper the extraction of shape elements is based on the polygonal decomposition of the interior of the object in the scene and on the description of its boundaries (edges). The interior decomposition is done using squared polygons (prime components). The boundaries of the object are determined using a modified Sobel operator. After the decomposition of the interior of the object and detecting its boundaries, a discrete set of points representing the edges and coordinates of the prime components is processed by a nonlinear algorithm. The purpose of this preprocessing is to remove the discontinuities and to produce a discrete skeleton of the object. The final stage of the extraction is to transform the discrete skeleton of the object into its shape description (shape descriptor). This process is carried out by matching the B-spline curves to the whole discreet skeleton of the object. The resulting shape descriptor of the object is composed of a set of extracted shape elements (set of B-spline curves) and an interrelation among them.
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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 | 0.001 |
| 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".