Bibliographic record
Abstract
In this paper, we consider a natural paradigm for lifting of crisp-set binary filters to fuzzy filters for hardware implementation and process the gray-scale realizations of binary images as [0,1]-valued fuzzy binary images. We present the implementation of the filtering algorithms for smoothing, peak detection and edge detection of such fuzzy images using the Xilinx Virtex series of FPGA for real-time processing of image sequences. The erosion filter forms the core for all of the filtering algorithms and the dilation filter itself is implemented as a function of the erosion filter. Smoothing is achieved using fuzzy opening of the input image using the user defined fuzzy structuring element. A fuzzy top-hat transform is used for peak detection. As opposed to gray-scale top-hat, which detects only the narrow peaks, the fuzzy top-hat is shown to detect both the narrow as well as wide peaks within the same image. Edge detection algorithm uses the fuzzy morphological gradient wherein the set minus operation has been performed between the dilated and the eroded images. Pipelined architectures are used for the erosion filter design and the use of flops has been maximized to achieve a high clock rate. The throughput measurements and the results generated by the implemented filters are also presented.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".