Higher order texture statistics impair contrast boundary segmentation
Bibliographic record
Abstract
Texture boundary segmentation is conventionally thought to be mediated by global differences in Fourier energy, i.e., low-order texture statistics. Here, we have examined the importance of higher order statistical structure of textures in a simple second-order segmentation task. We measured modulation depth thresholds for contrast boundaries imposed on texture samples extracted from natural scene photographs, using forced-choice judgments of boundary orientation (left vs. right oblique). We compared segmentation thresholds for contrast boundaries whose constituent textures were either intact or phase scrambled. In the intact condition, all the texture statistics were preserved, while in the phase-scrambled condition the higher order statistics of the same texture were randomized, but the lower order statistics were unchanged. We found that (1) contrast boundary segmentation is impaired by the presence of higher order statistics; (2) every texture shows impairment but some substantially more than others; and (3) our findings are not related to scrambling-induced changes in detectability. The magnitude of phase-scrambling effect for individual textures was uncorrelated with variations in their amplitude spectra, but instead we suggest that it might be related to differences in local edge structure or sparseness.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".