Gray-level Texture Characterization Based on a New Adaptive Nonlinear Auto-Regressive Filter
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Bibliographic record
Abstract
In this paper, we propose a new nonlinear exponential adaptive two-dimensional (2-D) filter for texturecharacterization. The filter adaptive coefficients are updated with the Least Mean Square (LMS) algorithm. Theproposed nonlinear model is used for texture characterization with a 2-D Auto-Regressive (AR) adaptive model. Themain advantage of the new nonlinear exponential adaptive 2-D filter is the reduced number of coefficients used tocharacterize the nonlinear image regarding the 2-D second-order Volterra model. Whatever the degree of the nonlinearity,the problem results in the same number of coefficients as in the linear case. The characterization efficiency ofthe proposed exponential model is compared to the one provided by both 2-D linear and Volterra filters and the cooccurrencematrix method. The comparison is based on two criteria usually used to evaluate the features discriminatingability and the class quantification in characterization techniques. The first criterion is proposed to quantify theclassification accuracy based on a weighted Euclidean distance classifier. The second criterion is the characterizationdegree based on the ratio of ";;;;;;;between-class";;;;;;; variances with respect to ";;;;;;;within-class";;;;;;; variances of the estimatedcoefficients. Extensive experiments proved that the exponential model coefficients give better results in texturediscrimination than several other parametric characterization methods even in a noisy context.Key words: Image Analysis, 2-D nonlinear filter, 2-D adaptive filter, texture characterization.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it