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Fiducial marker

About: Fiducial marker is a research topic. Over the lifetime, 3381 publications have been published within this topic receiving 77498 citations. The topic is also known as: fiducial marker & implanted fiducial.


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Journal ArticleDOI
TL;DR: A fiducial marker system specially appropriated for camera pose estimation in applications such as augmented reality and robot localization is presented and an algorithm for generating configurable marker dictionaries following a criterion to maximize the inter-marker distance and the number of bit transitions is proposed.

1,758 citations

Proceedings ArticleDOI
09 May 2011
TL;DR: This work describes a new visual fiducial system that uses a 2D bar code style “tag”, allowing full 6 DOF localization of features from a single image, incorporating a fast and robust line detection system, a stronger digital coding system, and greater robustness to occlusion, warping, and lens distortion.
Abstract: While the use of naturally-occurring features is a central focus of machine perception, artificial features (fiducials) play an important role in creating controllable experiments, ground truthing, and in simplifying the development of systems where perception is not the central objective. We describe a new visual fiducial system that uses a 2D bar code style “tag”, allowing full 6 DOF localization of features from a single image. Our system improves upon previous systems, incorporating a fast and robust line detection system, a stronger digital coding system, and greater robustness to occlusion, warping, and lens distortion. While similar in concept to the ARTag system, our method is fully open and the algorithms are documented in detail.

1,334 citations

Journal ArticleDOI
TL;DR: Two new expressions for estimating registration accuracy of point-based guidance systems and a surprising conclusion that expected registration accuracy (TRE) is worst near the fiducials that are most closely aligned are presented.
Abstract: Guidance systems designed for neurosurgery, hip surgery, and spine surgery, and for approaches to other anatomy that is relatively rigid can use rigid-body transformations to accomplish image registration. These systems often rely on point-based registration to determine the transformation, and many such systems use attached fiducial markers to establish accurate fiducial points for the registration, the points being established by some fiducial localization process. Accuracy is important to these systems, as is knowledge of the level of that accuracy. An advantage of marker-based systems, particularly those in which the markers are bone-implanted, is that registration error depends only on the fiducial localization error (FLE) and is thus to a large extent independent of the particular object being registered. Thus, it should be possible to predict the clinical accuracy of marker-based systems on the basis of experimental measurements made with phantoms or previous patients. This paper presents two new expressions for estimating registration accuracy of such systems and points out a danger in using a traditional measure of registration accuracy. The new expressions represent fundamental theoretical results with regard to the relationship between localization error and registration error in rigid-body, point-based registration. Rigid-body, point-based registration is achieved by finding the rigid transformation that minimizes "fiducial registration error" (FRE), which is the root mean square distance between homologous fiducials after registration. Closed form solutions have been known since 1966. The expected value (FRE/sup 2/) depends on the number N of fiducials and expected squared value of FLE, (FLE/sup 2/), but in 1979 it was shown that (FRE/sup 2/) is approximately independent of the fiducial configuration C. The importance of this surprising result seems not yet to have been appreciated by the registration community: Poor registrations caused by poor fiducial configurations may appear to be good due to a small FRE value. A more critical and direct measure of registration error is the "target registration error" (TRE), which is the distance between homologous points other than the centroids of fiducials. Efforts to characterize its behavior have been made since 1989. Published numerical simulations have shown that (TRE/sup 2/) is roughly proportional to (FLE/sup 2/)/N and, unlike (FRE/sup 2/), does depend in some way on C. Thus, FRE, which is often used as feedback to the surgeon using a point-based guidance system, is in fact an unreliable indicator of registration-accuracy. In this work the authors derive approximate expressions for (TRE/sup 2/), and for the expected squared alignment error of an individual fiducial. They validate both approximations through numerical simulations. The former expression can be used to provide reliable feedback to the surgeon during surgery and to guide the placement of markers before surgery, or at least to warn the surgeon of potentially dangerous fiducial placements; the latter expression leads to a surprising conclusion: Expected registration accuracy (TRE) is worst near the fiducials that are most closely aligned! This revelation should be of particular concern to surgeons who may at present be relying on fiducial alignment as an indicator of the accuracy of their point-based guidance systems.

1,055 citations

Journal ArticleDOI
TL;DR: Results show that the introduction of soft-tissue structures and interventional instruments into the phantom image can have a large effect on the performance of some similarity measures previously applied to 2-D-3-D image registration.
Abstract: A comparison of six similarity measures for use in intensity-based two-dimensional-three-dimensional (2-D-3-D) image registration is presented. The accuracy of the similarity measures are compared to a "gold-standard" registration which has been accurately calculated using fiducial markers. The similarity measures are used to register a computed tomography (CT) scan of a spine phantom to a fluoroscopy image of the phantom. The registration is carried out within a region-of-interest in the fluoroscopy image which is user defined to contain a single vertebra. Many of the problems involved in this type of registration are caused by features which were not modeled by a phantom image alone. More realistic "gold-standard" data sets were simulated using the phantom image with clinical image features overlaid. Results show that the introduction of soft-tissue structures and interventional instruments into the phantom image can have a large effect on the performance of some similarity measures previously applied to 2-D-3-D image registration. Two measures were able to register accurately and robustly even when soft-tissue structures and interventional instruments were present as differences between the images. These measures were pattern intensity and gradient difference. Their registration accuracy, for all the rigid-body parameters except for the source to film translation, was within a root-mean-square (rms) error of 0.53 mm or degrees to the "gold-standard" values. No failures occurred while registering using these measures.

912 citations

Proceedings ArticleDOI
20 Jun 2005
TL;DR: This proposed new marker system, ARTag has very low and numerically quantifiable error rates, does not require a grey scale threshold as does other marker systems, and can encode up to 2002 different unique ID's with no need to store patterns.
Abstract: Fiducial marker systems consist of patterns that are mounted in the environment and automatically detected in digital camera images using an accompanying detection algorithm. They are useful for augmented reality (AR), robot navigation, and general applications where the relative pose between a camera and object is required. Important parameters for such marker systems is their false detection rate (false positive rate), their inter-marker confusion rate, minimal detection size (in pixels) and immunity to lighting variation. ARTag is a marker system that uses digital coding theory to get a very low false positive and inter-marker confusion rate with a small required marker size, employing an edge linking method to give robust lighting variation immunity. ARTag markers are bi-tonal planar patterns containing a unique ID number encoded with robust digital techniques of checksums and forward error correction (FEC). This proposed new system, ARTag has very low and numerically quantifiable error rates, does not require a grey scale threshold as does other marker systems, and can encode up to 2002 different unique ID's with no need to store patterns. Experimental results are shown validating this system.

909 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023124
2022367
2021142
2020181
2019177
2018179