A hierarchical target recognition method based on image processing
Bibliographic record
Abstract
In order to provide an accurate and rapid target recognition method for some military affairs, public security, finance and otherdepartments, this paper studied firstly a variety of fuzzy signal, analyzed the uncertainties classification and their influence,eliminated fuzziness processing, presents some methods and algorithms for fuzzy signal processing, and compared with othermethods on image processing. Where, the fuzzy signal processing is that a blurred signal is dealt with by eliminating fuzziness.Moreover, this paper used the wavelet packet analysis to carry out feature extraction of target for the first time, extractedthe coefficient feature and energy feature of wavelet transformation, gave the matching and recognition methods, comparedwith the existing target recognition methods by experiment, and presented the hierarchical recognition method. In target featureextraction process, the more detailed and rich texture feature of target can be obtained by wavelet packet to image decompositionto compare with the wavelet decomposition. In the process of matching and recognition, the hierarchical recognition methodis presented to improve the recognition speed and accuracy. The wavelet packet transformation is used to carry out the imagedecomposition. Through experiment results, the proposed recognition method has the high precision, fast speed, and its correctrecognition rate is improved by an average 6.13% than that of existing recognition methods. These researches development inthis paper can provide an important theoretical reference and practical significance to improve the real-time and accuracy onfuzzy target recognition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".