Parallel implementation of image matching with MPI
Bibliographic record
Abstract
In this paper, the performance of parallel computing will be thoroughly discussed in the domain of image matching. The concept of image matching is widely used in the areas of security, medical and computer vision which require comparing two images for similarities. However, depending on the size of images, it is highly possible that the application computation cannot be handled in a single processor running a sequential algorithm. In order to overcome this limitation, parallel computing is introduced through the Message Passing Interface (MPI) library. In this project, for the comparison of two images, both images are first converted into grayscale and then are compared using the Sum of Square Differences (SSD) algorithm. Further, a parallel network of 12 processors was implemented for image matching and to calculate the performance of the SSD algorithm between both images. The performance gain of 12, 8, 4 and 2 processors was compared with the performance of a single processor. The comparison results presented a linear relationship between the performance gain and the number of processors used for execution. Hence, it proves that there are significant benefits of parallelism on SSD applications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".