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
Image fusion at the pixel level can be implemented as simply as an arithmetic average; however, there are many techniques that improve on the simple methods. For example, a weighted sum of two or more input images is a valid image fusion. Principal component analysis (PCA) can be applied to decide the weights of input images (i.e., which input is more significant). In these methods, input images are directly combined in the spatial (pixel) domain. This chapter fully describes two multiscale (also called multiresolution) fusion techniques: pyramids and wavelets. The multiscale fusion processes are usually performed in the transformed domain. Color image fusion and multi-image (three or more) fusion are introduced as examples. The chapter also highlights recent techniques that extend the wavelet concept, including bandelets and contourlets for image fusion. Finally, a set of fusion examples is presented for multimodal and multiscale image fusion.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it