An Area-Efficient FPGA Implementation of a Real-Time Multi-Class Classifier for Binary Images
Why this work is in the frame
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Bibliographic record
Abstract
Developing image classification modules in embedded systems is a complex task due to the limited resources available. In this brief, a multi-class image classifier using HOG feature extractor and SVM classifier is proposed for binary images. The novelty of the proposed system is applying two steps of binarization to the HOG technique to improve processing speed and area efficiency. First, HOG features are extracted from binary images to simplify the feature extraction process. Second, block normalization of the HOG is replaced with binarization to reduce hardware resource utilization. Compared to a similar existing work, our system speeds up the classification process while utilizing fewer hardware resources, with an 11.4% higher classification accuracy using the same setting.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it