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
Abstract We present a new 3D adaptive filtering approach capable of detecting and removing impulsive noise in image/video sequences. The proposed method takes advantage of switching median schemes and robust lower‐upper‐middle (LUM) smoothing characteristics. Simulation studies reported in this article indicate that the proposed filtering scheme achieves an excellent trade‐off between noise attenuation and detail preserving characteristics, and clearly outperforms previously introduced approaches in terms of subjective and objective image quality measures. Besides the filter analysis and the testing of its performance, an important part of this article discusses the filter implementation in Altera field programmable logic devices (FPLD). Simulation studies indicate that the proposed method can be efficiently implemented in hardware and is suitable for real‐time image/video processing applications. © 2005 Wiley Periodicals, Inc. J Imaging Syst Technol 14, 223–237, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20027
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.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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