Fingerprint image compression standard based on wave atoms decomposition and self organizing feature map
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multiresolution analysis tools have been successfully applied to fingerprint image compression for more than a decade. Fingerprint image compression and reconstruction standards used by the US Federal Bureau of Investigation (FBI) are based upon the popular 9/7 discrete wavelet transform (DWT). A novel fingerprint image compression technique based on recently proposed multiresolution analysis tool i.e. wave atoms decomposition is proposed. Wave atoms decomposition has been specifically designed for enhanced representation of oscillatory patterns and to convey temporal and spatial information more precisely. Our proposed compression scheme is based upon vector quantization of decomposed wave atoms representation of fingerprint images using self organizing feature map (SOM). The quantized information is later encoded using arithmetic entropy scheme. The proposed compression scheme outperforms the FBI fingerprint image compression standard, the wavelet scalar quantization (WSQ). Data mining, law enforcement, border security, and forensic applications can potentially benefit from our proposed compression technique.
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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 | 0.000 | 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) | 0.000 | 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