Bibliographic record
Abstract
Image fusion is well known in nature as a means for rapid and robust interaction with the environment. Numerous animals are afforded image fusion through biological design. The motivation for machine image fusion has its origins in biologically inspired animal vision and human perception through neurological design. Although there are many available websites of prominent publications in science and engineering that describe biological image fusion, this chapter serves as a brief review to motivate readers. Many more detailed illustrations of biological sensors, neural pathways, and animal experiments are available from various researchers and websites as sources to further clarify the information in this chapter. Animal examples include visual and IR fusion in snakes, polarization in mantis shrimp, and visual and ultraviolet (UV) in butterflies. Some animals, like elephants, are arrhythmic, which means that their vision changes with the time of day to respond to varying light levels. Likewise, the human brain has detailed mechanisms for binocular fusion, feature fusion, contextual-object-detection fusion, and movement fusion. For example, human fusion perception results in fovea/peripheral integration, illusions, and colorization. This chapter identifies motivating examples from nature and concludes with biological image fusion approaches, including center/surround and opponent processing with biologically inspired neural networks.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.087 | 0.072 |
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".