Object representation based on gabor wave vector binning: An application to human head pose detection
Bibliographic record
Abstract
Visual object recognition is a hard computer vision problem. In this paper, we investigate the issue of the representative features for object detection and propose a novel discriminative feature sets that are extracted by accumulating magnitudes for a set of specific Gabor wave vectors in 1-D histogram defined over a uniformly-spaced grid. A case study is presented using radial-basis-function kernel SVM as base learners of human head poses. In which, we point out the effectiveness of the proposed descriptors, relative to related approaches. The average performance reached 65% for yaw and 73.3% for pitch, which are better than the (40.7% and 59.0%) accuracy achieved by calibrated people. A substantial performance gain as higher as (1.18% for yaw and 1.27% for pitch) is achievable with the proposed feature sets.
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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".