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Home / Equine Research Bank / Sensors (Basel) / Optimising Camera-ChArUco Geometry for Motion Compensation i...
Sensors (Basel, Switzerland)2026; 26(4); 1310; doi: 10.3390/s26041310

Optimising Camera-ChArUco Geometry for Motion Compensation in Standing Equine CT: A CT-Motivated Benchtop Study.

Abstract: Standing equine computed tomography (CT) acquisitions are susceptible to residual postural sway, which can introduce view-inconsistent motion and degrade image quality. External optical tracking based on ChArUco fiducials is a promising, low-cost strategy to enable projection-wise motion compensation, yet quantitative guidance on how camera-marker geometry affects pose-estimation performance remains limited. This CT-motivated benchtop study characterizes how the relative camera-ChArUco configuration influences both the accuracy (bias with respect to ground truth) and the precision (repeatability) of pose estimates obtained from RGB images using OpenCV ChArUco detection and reprojection-error minimization to estimate the rigid camera-to-board transformation. Controlled experiments systematically varied acquisition protocol (continuous repeated estimates at fixed pose versus cyclic repositioning), viewing angle over a wide angular range at two working distances, and camera-to-board distance over multiple depth settings. Ground truth for angular configurations was defined by a stepper-motor rotation stage, while distance ground truth was obtained by ruler measurements. Performance was summarized via mean absolute error and standard deviation across repeated measurements, complemented by variance-based statistical testing with multiple-comparison correction. Cyclic repositioning did not yield evidence of increased variability relative to continuous acquisitions, supporting view-by-view sampling. Viewing angle induced a consistent accuracy-precision trade-off for rotations: frontal views minimized mean error but exhibited higher variability, whereas oblique views reduced jitter at the expense of increased bias. Increasing working distance reduced repeatability, most prominently for depth-related components. Overall, these findings provide pre-clinical guidance for selecting camera/marker placement (moderately oblique viewpoints, limited working distance, sufficient image footprint) before in-scanner and in-vivo validation for standing equine CT motion compensation.
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Publication Date: 2026-02-18 PubMed ID: 41755249PubMed Central: PMC12943937DOI: 10.3390/s26041310Google Scholar: Lookup
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  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This study investigates how the positioning and geometry of a camera relative to ChArUco fiducial markers affect the accuracy and precision of motion tracking used to compensate for motion during standing equine CT scans.
  • The research uses a benchtop experimental setup simulating CT conditions to provide quantitative guidance on optimal camera-marker configurations for improving motion compensation techniques.

Background and Motivation

  • Standing equine CT scans are prone to residual postural sway from the horse, causing inconsistent motion between views and degrading image quality.
  • External optical tracking systems that use ChArUco markers—specialized AprilTags combined with chessboard patterns—offer a low-cost way to track motion with high spatial accuracy.
  • Effective motion compensation requires precise pose estimation, but it is unclear how the physical arrangement (geometry) of the camera relative to these markers influences estimation performance.
  • This gap motivated a controlled benchtop study to emulate CT imaging considerations and systematically evaluate various camera-marker geometries.

Methodology

  • Pose Estimation Technique:
    • OpenCV’s ChArUco detection system was used to identify fiducial points from RGB images.
    • Rigid transformations between camera and marker board were estimated by minimizing reprojection errors.
  • Experimental Setup:
    • A benchtop rig simulated CT acquisition conditions with precise control of angles and distances.
    • Ground truth for angular orientation was provided by a stepper-motor rotation stage for precise rotational offsets.
    • Distance ground truth was measured manually using rulers for accuracy assessment.
  • Variables Tested:
    • Acquisition protocol: continuous repeated measurements at a fixed pose versus cyclic repositioning of the board.
    • Viewing angle: varied over a wide angular range, including frontal and oblique perspectives.
    • Camera-to-board distance: multiple depths were tested to emulate different working distances.
  • Performance Metrics:
    • Accuracy: measured as bias (mean absolute error) compared to ground truth pose.
    • Precision: measured as repeatability or variability (standard deviation) across repeated samples.
    • Statistical testing: variance-based tests with corrections for multiple comparisons were used to ascertain significance of differences.

Key Findings

  • Repositioning Results:
    • No significant increase in variability was found when cyclic repositioning was used versus continuous measurements at a fixed pose.
    • This supports the feasibility of acquiring view-by-view pose estimates during CT scans without loss in measurement precision.
  • Viewing Angle Effects:
    • Frontal camera views minimized the mean pose error, resulting in higher accuracy but showed higher variability (worse precision).
    • Oblique viewpoints reduced jitter (improved precision) but introduced an increase in systematic bias (lower accuracy).
    • This illustrates a trade-off between accuracy and precision depending on camera angle, suggesting that moderate oblique angles may be ideal.
  • Distance Effects:
    • Increasing camera-to-marker working distance generally reduced repeatability, especially for depth-related pose components.
    • This suggests working distances should be kept limited to improve the consistency of motion estimates.
  • Overall Optimal Configuration:
    • Moderately oblique viewing angles combined with limited working distance offer a good balance of accuracy and precision.
    • Ensuring sufficient image footprint (marker size and resolution in the camera image) is important for robust detection and pose estimation.

Implications and Future Directions

  • The study provides quantitative, pre-clinical recommendations for camera and ChArUco marker placement in standing equine CT environments.
  • Using these findings, researchers and clinicians can better design optical tracking setups to enable real-time, projection-wise motion compensation during scans.
  • Next steps include validating these optimized configurations in actual CT scanners with live equine subjects to confirm motion compensation effectiveness in vivo.
  • Improved motion tracking can lead to higher-quality CT images, aiding diagnosis and treatment planning in equine veterinary medicine.

Cite This Article

APA
Aliani C, Lorenzetto Bologna C, Francia P, Bocchi L. (2026). Optimising Camera-ChArUco Geometry for Motion Compensation in Standing Equine CT: A CT-Motivated Benchtop Study. Sensors (Basel), 26(4), 1310. https://doi.org/10.3390/s26041310

Publication

Sensors (Basel, Switzerland)
ISSN: 1424-8220
NlmUniqueID: 101204366
Country: Switzerland
Language: English
Volume: 26
Issue: 4
PII: 1310

Researcher Affiliations

Aliani, Cosimo
  • Department of Information Engineering, University of Florence, Via di Santa Marta 3, 50139 Florence, Italy.
Lorenzetto Bologna, Cosimo
  • Epica Imaginalis, Via Rodolfo Morandi 13/15, 50019 Florence, Italy.
Francia, Piergiorgio
  • Department of Information Engineering, University of Florence, Via di Santa Marta 3, 50139 Florence, Italy.
Bocchi, Leonardo
  • Department of Information Engineering, University of Florence, Via di Santa Marta 3, 50139 Florence, Italy.

MeSH Terms

  • Horses
  • Tomography, X-Ray Computed / methods
  • Animals
  • Image Processing, Computer-Assisted / methods
  • Motion
  • Algorithms

Conflict of Interest Statement

Author Cosimo Lorenzetto Bologna was employed by the company Epica Imaginalis. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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