Applications of Multiscale Geometric Analysis in Image Texture Recognition and Classification
Bibliographic record
Abstract
With the rapid development of computer vision, the applications of image texture recognition and classification are increasingly prevalent across various domains, particularly in medical imaging, industrial inspection, and remote sensing image analysis, these applications hold significant practical importance.Traditional texture recognition techniques often rely on manually designed feature extraction methods, which tend to perform poorly in complex environments, are sensitive to noise and lighting variations, and are limited when dealing with non-uniform or multiscale textures.To address these shortcomings, this paper introduces two novel texture analysis methods that enhance the robustness of texture features and improve classification accuracy.The first part of the study presents the contourlet-kernel spectral regression (KSR) image texture feature extraction technique, which, by integrating Contourlet transform with Krawtchouk polynomials, effectively enhances the descriptive power and adaptability of features.The second part explores a texture image classification method based on domain-multiresolution cooccurrence matrices (MCM), which significantly improves the accuracy and robustness of the classification process by analyzing the co-occurrence characteristics of images at multiple resolutions.The introduction of these methods not only optimizes texture recognition performance but also advances the application of image processing technologies in complex scenarios.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".