An Enhanced Histogram of Oriented Gradient Descriptor for Numismatic Applications
Bibliographic record
Abstract
The Histogram of Oriented Gradients (HOG) is one of the most widely used methods to extract the gradient features for object recognition and consistently shows high accuracy rates when compared to other descriptors. The major drawbacks of using the HOG method are the necessity of finding an optimal window size to fit the whole object; and the exhaustive search mechanism represented by a fixed window size sliding through the whole image to locate and recognize objects. This research proposes an efficient and robust Dynamic-HOG as an improvement to the traditional HOG method to locate and recognize structured objects in images. The proposed method works by locating and analyzing the structured objects in images in order to define a dynamic window size w.r.t. each object size. Moreover, the Dynamic-HOG method requires much less processing time by eliminating the exhaustive search mechanism. The method defines the height and width thresholds of objects and bounds each object with a window w.r.t. its size while ignoring non–object edges. It fits structured objects of a close range of heights and widths. This paper considers the characters that are minted on coins of different languages and sizes as the objects to recognize. There are several papers in the literature discussing coin recognition problem and proposing solutions based on various sets of features extracted from the entire coin image. This research also proposes a new method for coin recognition by focusing on recognize coins based on smaller part of the coin image which are the characters. Our method is evaluated on coins from diverse countries with different background complexity. The proposed method achieved precision and recall rates as high as 98.08 and 98.23%, respectively; which demonstrate the effectiveness and robustness of the proposed method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 0.002 |
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".