A Quantum Particle Swarm Optimization and Genetic Algorithm approach to the correspondence problem
Bibliographic record
Abstract
Finding correspondences between deformable objects has wide application in many domains. In information retrieval, researchers may be interested in finding similar objects, while computer animation experts may be considering ways to morph shapes. The correspondence problem is especially challenging when the objects under consideration are suspect to non-rigid deformations, noise and/or distortions. In this paper, a novel method using Quantum Particle Swarm Optimization (QPSO) and Genetic Algorithms (GA) is presented to address this issue. In our QPSO-GA algorithm we formulate the problem of correspondence detection as an optimization problem over all possible mapping in between the geodesic distance matrices associated with two sets of point clouds. We proceed to identify the optimal mapping, by first applying Quantum Particle Swarm Optimization to the permutation matrices associated with their geodesic distance matrices and then employing Genetic Algorithms in order to guide the search. Experimental results suggest that our QPSO-GA algorithm is fast, scalable, and robust. Our method accurately identifies the correspondences between objects, even in the presence of noise and distortion.
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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 | 0.000 | 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 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".