A NEW EDGE PRESERVING BINARY IMAGES RESIZING TECHNIQUE
Why this work is in the frame
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Bibliographic record
Abstract
Efficient methods for resizing two-dimensional binary signals are increasingly on demand for a variety of applications such as computer graphics, computer cartography, and machine generated text. 2,4,6 Recently, algorithms have been proposed such as those based on interpolation methods including nearest neighbor, linear, and Butterworth. Other methods such as splines, 5 wavelets, and DCT-based algorithms 11,12 are also presented. All these methods generate distortion and noticeable degradation in the quality of the signals (e.g., binary images) especially at and around edges. In this paper, we present a new near optimal edge preserving binary image resizing scheme that produces perceptually perfect edges. This scheme is based on edge detection, edge chain coding, edge code representation, and the uses of predetermined resizing patterns. The results obtained by this method show that the resized images are aesthetically and objectively much better than the results of other published methods.
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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 | 0.001 |
| 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