Object detection by parts using appearance, structural and shape features
Bibliographic record
Abstract
This paper proposes a novel object detection algorithm with the combination of appearance, structural and shape features. We follow the paradigm of object detection by parts, which first uses detectors developed for individual parts of an object and then imposes structural constraints among the parts for the detection of the entire object. In our research, we further incorporate a priori shape information about the object parts for their detection, in order to improve the performance of the object detector. Specifically, we use an HOG-based detector for object parts whose output, together with structural constraints, is then used to seed a subsequent image segmentation step in order to delineate the potential object parts. To determine whether the segmented regions are indeed object parts, we train a part classifier using shape features of object parts and a support vector machine (SVM). The detection of the object is determined by combining the likelihoods computed with the HOG part detector, the shape-based part classifier, and the structural constraints among the parts. For validation of our object detection algorithm, we apply it to the detection of the tooth line of a mining shovel, which consists of a set of teeth with known relative position and orientation from each other, under various lighting conditions. The experimental results demonstrate that our system is able to improve the detection performance significantly when part shape information is used.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".