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Record W2093671867 · doi:10.1115/imece2006-15763

Integration of Vision and Inertial Sensors for a Surgical Tool Tracking

2006· article· en· W2093671867 on OpenAlexaff
N. Parnian, M. F. Golnaraghi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsInertial measurement unitKalman filterComputer scienceComputer visionArtificial intelligenceOrientation (vector space)Tracking systemCalibrationReliability (semiconductor)Tracking (education)Machine visionMatch movingPosition (finance)Motion (physics)

Abstract

fetched live from OpenAlex

This paper represents a hybrid Vision/INS system in a microsurgical tool tracking application. Surgical MEMS devices must not only cope with all of the challenges that conventional MEMS devices have, but also address the integration of electronics and signal processing, calibration, reliability, accuracy and testing. A hybrid Vision/INS system with the integration of the Extended Kalman Filter precisely calculates 6D position-orientation of a microsurgical tool during surgery. This configuration guarantees the real-time tracking of the instrument. Ultimately, the vision system supports the IMU to deal with the drift problem but the position error increases dramatically in the absence of the vision system. In this paper, the tool motion modeling is proposed to bind the error in the acceptable range for a short period of missing data. The motion of the tool is modeled and updated at any time that the instrument is in the camera view field. This model is applied to the estimation algorithm whenever the camera is not in line of site and the optical data is missing.

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.

How this classification was reachedexpand

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.892
Threshold uncertainty score0.129

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.016
GPT teacher head0.286
Teacher spread0.270 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2006
Admission routes1
Has abstractyes

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