2011 Canadian Conference on Computer and Robot Vision Object Detection Using Principal Contour Fragments
Bibliographic record
Abstract
Abstract—Contour features play an important role in object recognition. Psychological experiments have shown that maximum-curvature points are most distinctive along a contour [6]. This paper presents an object detection method based on Principal Contour Fragments (PCFs), where PCFs are extracted by partitioning connected edge pixels at maximumcurvature points. An object is represented by a set of PCFs and their mutual geometric relations. The mutual geometric relations are described in each PCF’s local coordinate system, and they are invariant to translation, rotation, and scale. With this representation, given any individual PCF, the system is capable of predicting all other PCFs ’ geometric properties. Object instances are detected in test images by sequentially locating PCFs whose geometric properties best match their predictions. Detected objects are verified according to their similarity to the model based on both individual PCF descriptors and mutual relation descriptors. Evaluation results show that the system works well in the presence of background clutter, large scale changes, and intra-class shape variations. Keywords-object detection; shape matching; contour matching; edge matching. I.
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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".