Description and Recognition of Symmetrical and Freely Oriented Images Based on Parallel Shift Technology
Bibliographic record
Abstract
The method of description and recognition of images based on the technology of parallel shift is described. The parallel shift technology allows only one characteristic for describing of images. The feature is the area of the image, which is determined by the number of cells belonging to the image. The main characteristics of the complex image area are described. The problem of using parallel shift technology is the inability to recognize symmetrical images and images with free orientation. In accordance with the problem in the paper a method is described that allows to recognize the orientation of the image, as well as recognizing symmetrical images that have the same functions of area of intersection. To solve the problem, additional elements are introduced on one of the edges of the image, which in a small amount distinguish it from the original image, and additional quantitative characteristics of the area are introduced. The additional elements are introduced only on one of the edges of the image for all images at the system input. For each rotated and symmetrical image with equal functions, the intersection areas a new intersection functions are defined. Differences in the functions of the areas of intersection of both images are determined and on the based on the obtained quantitative characteristics of the function of the area of intersection of the images the shape of the image are determined. To form the intersection function of the areas of the modified image, the number of shifts is increased by one, and also the function change occurs at each step in accordance with the introduced additional elements. The conducted research showed high reliability of image recognition.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".