Author manuscript, published in "International Symposium on Optomechatronic Technologies, ISOT'10., Toronto: Canada (2010)" Autofocusing-based visual servoing: application to MEMS micromanipulation
Bibliographic record
Abstract
Abstract — In MEMS microassembly areas, different methods of automatic focusing are presented in the literature. All these methods have a common point. Thus, the current autofocusing methods for microscopes need to perform a scanning on all the vertical axis of the microscope in order to find the peak corresponding to the focus (sharpen image). Those methods are time consuming. Therefore, this paper presents an original method of autofocusing based on a velocity control approach which is developed and validated on real experiments. I. OVERVIEW Reliable autofocusing methods are indispensable in the microassembly of hybrid micro-electromechanical systems (MEMS) and for general uses of optical microscopes [6], [8]. Ensuring an optimal focus is essential in industrial vision systems. A good focus ensures sharpen image and thus
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.362 | 0.155 |
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".