Improvement of Accuracy for 2D Marker-Based Tracking Using Particle Filter

Uematsu, Yuko; Saitō, Hideo

doi:10.1109/icat.2007.16

Cited by 39 publications

(11 citation statements)

References 7 publications

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“…On one hand, the three dimensional structure of the self-encoded marker provided additional information to describe the position and orientation of the feature points. Planar markers, like the checkerboard marker, have a poor conditioning, when being parallel to the camera image plane (Uematsu, Y., and Saito, H., 2007). Since the self-encoded marker was also planar in the direction of motion of the pitch experiment, a worse accuracy was observed.…”

Section: Discussionmentioning

confidence: 99%

Self-encoded marker for optical prospective head motion correction in MRI

Forman

Aksoy

Hornegger

et al. 2011

Medical Image Analysis

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The tracking and compensation of patient motion during a magnetic resonance imaging (MRI) acquisition is an unsolved problem. For brain MRI, a promising approach recently suggested is to track the patient using an in-bore camera and a checkerboard marker attached to the patient’s forehead. However, the possible tracking range of the head pose is limited by the fact that the locally attached marker must be entirely visible inside the camera’s narrow field of view (FOV). To overcome this shortcoming, we developed a novel self-encoded marker where each feature on the pattern is augmented with a 2-D barcode. Hence, the marker can be tracked even if it is not completely visible in the camera image. Furthermore, it offers considerable advantages over the checkerboard marker in terms of processing speed, since it makes the correspondence search of feature points and marker-model coordinates, which are required for the pose estimation, redundant. The motion correction with the novel self-encoded marker recovered a rotation of 18° around the principal axis of the cylindrical phantom in-between two scans. After rigid registration of the resulting volumes, we measured a maximal error of 0.39 mm and 0.15° in translation and rotation, respectively. In in-vivo experiments, the motion compensated images in scans with large motion during data acquisition indicate a correlation of 0.982 compared to a corresponding motion-free reference.

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Section: Discussionmentioning

confidence: 99%

Self-encoded marker for optical prospective head motion correction in MRI

Forman

Aksoy

Hornegger

et al. 2011

Medical Image Analysis

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“…The position and size of image markers may affect the image marker pose estimated by computer vision systems [5] and thus the accuracy of the registration of virtual models with real-world features in AR applications. Uematsu et al [6] argued that detection of image markers becomes unstable when they lie on the central axis of the camera view frustum. They attributed this effect to slight differences in the detection of image features (e.g.…”

Section: Position and Size Of Image Markersmentioning

confidence: 99%

Effect of marker position and size on the registration accuracy of HoloLens in a non-clinical setting with implications for high-precision surgical tasks

et al. 2021

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Purpose Emerging holographic headsets can be used to register patient-specific virtual models obtained from medical scans with the patient’s body. Maximising accuracy of the virtual models’ inclination angle and position (ideally, ≤ 2° and ≤ 2 mm, respectively, as in currently approved navigation systems) is vital for this application to be useful. This study investigated the accuracy with which a holographic headset registers virtual models with real-world features based on the position and size of image markers. Methods HoloLens® and the image-pattern-recognition tool Vuforia Engine™ were used to overlay a 5-cm-radius virtual hexagon on a monitor’s surface in a predefined position. The headset’s camera detection of an image marker (displayed on the monitor) triggered the rendering of the virtual hexagon on the headset’s lenses. 4 × 4, 8 × 8 and 12 × 12 cm image markers displayed at nine different positions were used. In total, the position and dimensions of 114 virtual hexagons were measured on photographs captured by the headset’s camera. Results Some image marker positions and the smallest image marker (4 × 4 cm) led to larger errors in the perceived dimensions of the virtual models than other image marker positions and larger markers (8 × 8 and 12 × 12 cm). ≤ 2° and ≤ 2 mm errors were found in 70.7% and 76% of cases, respectively. Conclusion Errors obtained in a non-negligible percentage of cases are not acceptable for certain surgical tasks (e.g. the identification of correct trajectories of surgical instruments). Achieving sufficient accuracy with image marker sizes that meet surgical needs and regardless of image marker position remains a challenge.

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“…Unlike the virtual reality (VR), the augmented reality (AR) is an environment containing virtual and real objects simultaneously [5]. By using Augmented Reality we can see the virtual object as if they really exist in the real world [1].…”

Section: Introductionmentioning

confidence: 99%

Development of interaction and orientation method in welding simulator for welding training using augmented reality

Baskoro

Amat

Kuswana

2014

2014 International Conference on Advanced Computer Science and Information System

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Abstrack-This paper presents a method of interaction and orientation that can be implemented in welding simulator and will be used for basic welding training. In welding process there are many factors that can affect the welding results. One of them is the orientation angle and interaction distance between plate and torch. Therefore, a method to find an angle and a distance between plate and welding torch so then it can be used as a part of the welding simulator was developed. The required components are webcam in laptop used as a video tracking device, two sets of marker, and ARToolKit as software library. After the interaction and orientation method was successfully built, a measurement for the interaction method to get a relation between actual distance and virtual distance was performed. The result will receive a comparison value from 27 to 28 pixels per centimeter with specified limitation. Then for orientation method, standard deviation and root mean square error (RMSE) of an angle was measured. This is the first stage of development in welding simulator. Further development is required to get a more advanced welding simulator.

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Improvement of Accuracy for 2D Marker-Based Tracking Using Particle Filter

Cited by 39 publications

References 7 publications

Self-encoded marker for optical prospective head motion correction in MRI

Self-encoded marker for optical prospective head motion correction in MRI

Effect of marker position and size on the registration accuracy of HoloLens in a non-clinical setting with implications for high-precision surgical tasks

Development of interaction and orientation method in welding simulator for welding training using augmented reality

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