Use of Wavelet Packet Transform in Characterization of Surface Quality
Bibliographic record
Abstract
Feature extraction is a crucial step in pattern recognition problems as well as in methods for characterizing the quality of a product surface (Liu, J. Ph.D. thesis, McMaster University, Canada, 2004). In this paper, different types of wavelet transforms, that is, the wavelet packet transform and the discrete wavelet transform, are compared in the feature extraction step for classification of the surface quality of rolled steel sheets (Bharati, M.; et al. Chemom. Intell. Lab. Syst. 2004, 72, 57−71). Using this real-world industrial example, we have experimentally shown that the wavelet packet transform is superior to the discrete wavelet transform in terms of classification performance and Fisher's criterion. We also propose an easy but powerful method to determine the optimal decomposition level. A closer look at the characteristics of the image data reveals that as a result of its equal frequency bandwidth, wavelet packet transform is more suitable for extracting textural features when textural information from different classes of images is not confined within a certain (spatial) frequency region.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".