On the Interference of Task-Irrelevant Hue Variation on Texture Segmentation
Bibliographic record
Abstract
Although natural images often include discordant information about object boundaries, the majority of research on texture segmentation has involved variation along a single dimension, e.g. colour, orientation, size. In this study, we examined orientation-based texture segmentation in the presence and absence of task-irrelevant colour variation. Previously, it had been shown that orientation-based texture segmentation was impaired if the elements, normally of one colour, were randomly allocated one of two colours (Morgan et al, 1992 Proceedings of the Royal Society of London, Series B 248 291-295). We found that this interference disappeared, however, when the spatial pattern of the colour variation was regular, as opposed to random, and when the elements were randomly positioned. We consider four models of how relevant and irrelevant texture information might combine to produce the interference effect, with special regard to these new findings. None of the models could account for the dependency of the interference effect on the spatial arrangement of colour and orientation in the texture. We suggest that inter-element separation and spatial-frequency selectivity are critical variables in the interference effect.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".