Wavelet-based image target detection methods
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
Detection of small, faint, and/or obscured targets in a sequence of noisy images is not trivial. In this case, target and background (texture) features are generally undistinguishable in the original image domain. So, the image has to be transformed to a domain in which those features can be separable. The wavelet transform has been shown to be an excellent methodology that image segmentation can be performed through exploiting the wavelet multi-scale analysis capability. This paper reviews general wavelet-based methods for image target detection. Although the paper reviews the most recent target detection methods using wavelet, which are available in open literature, it focuses on illustrating the different ideas of using wavelet coefficients as a tool for target-background separation. Furthermore, this paper has the objective to offer a quick look to the many approaches, to put in light the authors' most recent developments in this field, and to serve as a background for new advances.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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