Bibliographic record
Abstract
We can visually recognize a variety of surface state. Just a quick look is sufficient to find the bath floor is dry, the road in front is slippery, the window glass is frosty, or the ornament is dusty. If a given surface state perception relies on the analysis of diagnostic image features, an effective strategy to reveal those features and the associated visual processing is to find a stimulus transformation that alters the apparent surface state. Here we report an image transformation that makes dry objects look wet. This wet filter consists of two operations: (1) Tone-remapping with an accelerating nonlinear function that renders the intensity histogram positively skewed: (2) color saturation enhancement. In an experimental test, we applied the wet filter to a variety of natural textures of the McGill Calibrated Colour Image Database. The results of a wetness rating experiment showed that the wet-filtered images were perceived as wetter than the original images. In addition, the perceived wetness depended on the variance of hue. The wet-filter was less effective for images with a small variance of hue. Optically, wetting a surface tends to increases the specular reflection. In addition, as the incoming light scatters repeatedly within the surface liquid layer, the light going out from the surface tends to be darker and more saturated. The effects of these optical changes can be simulated by the two wet-filter operations. However, positively skewed luminance histogram and high chromatic saturation may be caused by other factors — for instance, the visual scene may happen to include highly saturated glossy objects. This is presumably why hue variation matters. If the same image transformation simultaneously occurs in many different objects, the brain infers that the change likely has the same cause, such as water shower in the present case. Meeting abstract presented at VSS 2015
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".