FPGA-based Implementation of HOG Algorithm: Techniques and Challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Histogram of Oriented Gradients (HOG) is a method for extracting features from an image, which has many applications in Computer Vision. Due to the complexity and high amount of computations of this algorithm, software-based implementations of HOG cannot meet the real-time criterion. Therefore, many researchers have implemented HOG algorithm on hardware platforms such as FPGAs. This paper presents an extensive review of FPGA-based implementations of the HOG algorithm, that have been published from 2010 to 2019. Different techniques for hardware implementation of HOG are classified into three groups: methods which improve a certain stage of the algorithm, methods which optimize the whole algorithm, and methods which make minor simplification on the algorithm. In this paper, these three classes of techniques are reviewed. Finally, the speed and resource utilization of the surveyed papers are compared to each other in order to present a comprehensive conclusion on FPGA-based HOG implementation.
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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 | 0.000 | 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 | 0.000 |
| 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 it