Content-independent orientation detection with histogram of optimized local binary pattern
Bibliographic record
Abstract
This thesis is primarily concerned with the introduction of a new approach to the general problem of automatic image orientation detection. Inspired by the local binary pattern (LBP), a luminance, rotation and scale invariant and content-independent algorithm is proposed, namely: Histogram of Optimized Local Binary Pattern (HOOPLBP). Whilst the proposed approach is essentially generic, the core application considered in this study is human face orientation detection. To detect the face orientation, a general face model is trained using the HOOPLBP feature. The experiment show a very impressive result. Integrating this result with other face related techniques will facilitate some applications. To this end, this thesis propose a hybrid face detection system. Specifically, the new system aims to detect both upright and tilted human face in digital images. In the scheme, several face related algorithms are integrated to achieve difference tasks in different stages. In addition, two modified systems are used in this thesis to detect faces in both grayscale images and color images. The HOOPLBP is a new and robust method in automatic image orientation detection. It can be improved by other techniques and also can be used in many other fields. The future work is also included in the thesis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".