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Record W2105947624 · doi:10.1017/s1431927612000220

An Improved Visual Tracking Method in Scanning Electron Microscope

2012· article· en· W2105947624 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMicroscopy and Microanalysis · 2012
Typearticle
Languageen
FieldEngineering
TopicImage Processing Techniques and Applications
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsSubpixel renderingRobustness (evolution)Computer visionTracking (education)Computer scienceArtificial intelligenceVisual servoingClutterTemplate matchingMicroscopeOpticsPixelImage (mathematics)Physics

Abstract

fetched live from OpenAlex

Since their invention, nanomanipulation systems in scanning electron microscopes (SEMs) have provided researchers with an increasing ability to interact with objects at the nanoscale. However, most nanomanipulators that are capable of generating nanometer displacement operate in an open-loop without suitable feedback mechanisms. In this article, a robust and effective tracking framework for visual servoing applications is presented inside an SEM to achieve more precise tracking manipulation and measurement. A subpixel template matching tracking algorithm based on contour models in the SEM has been developed to improve the tracking accuracy. A feed-forward controller is integrated into the control system to improve the response time. Experimental results demonstrate that a subpixel tracking accuracy is realized. Furthermore, the robustness against clutter can be achieved even in a challenging tracking environment.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.325
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it