ARTag, a fiducial marker system using digital techniques

By Mark Fiala. 2005

1,039 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

RenderGAN: Generating Realistic Labeled Data (2016, background): “The markers in used were originally described as fiducial markers in augmented reality systems (Fiala, 2005) and rely on spatial derivatives to detect the rectangular outline of a tag.”
Mobile Augmented Reality Survey: From Where We Are to Where We Go (2017, methodology): “tracking, such as ARToolkit [154], ARTag [206], ARToolKitPlus [162] and OpenTracker [150].”
AprilTag 2: Efficient and robust fiducial detection (2016, methodology): “ARTag [2] attempted to rectify the problem of inter-tag confusion by introducing a 2D binary barcode pattern.”
Pose Estimation for Augmented Reality: A Hands-On Survey (2016, methodology): “In the literature, SIFT [72], which has been considered a breakthrough for 2D points matching, was proposed in 1999 and then various types of keypoint detectors and descriptors have been considered.”
Adaptive Thresholding using the Integral Image (2007, methodology): “For example, it is often one of the first steps in marker-based augmented reality systems [Billinghurst et al. 2001; Bradley and Roth 2004; Fiala 2005], and it has been used in high dynamic range photography [Ward 2003].”
AprilTag: A robust and flexible visual fiducial system (2011, methodology): “Visual fiducial systems are perhaps best known for their application to augmented reality, which spurred the development of several popular systems including ARToolkit [2] and ARTag [3].”
Multiple Viewpoint Recognition and Localization (2010, positive): “Instead, we have constructed a 3D target from highly unique visual patterns similar to those described in [22–24].”
Evaluation of Interest Point Detectors and Feature Descriptors for Visual Tracking (2011, background): “Augmented Reality Tracking for AR was first investigated using fiducial markers (Kato and Billinghurst 1999; Fiala 2005).”
Robust monocular SLAM in dynamic environments (2013, background): “Many of the previous works require the prior knowledge of the target scene, such as point cloud [9], CAD model [30], or artificial markers [12], etc.”
Technology for Classroom Orchestration (2010, background): “All objects (shelves, pillars, cardboard) are tagged with fiducial markers (similar to a 2 dimensional bar code) which enable a camera to track their position on the table (Fiala, 2005) and enable the system to project graphical representations (augmentations) on top and around the objects.”
BEEtag: A Low-Cost, Image-Based Tracking System for the Study of Animal Behavior and Locomotion (2015, methodology): “barcodes and Quick-Response, or QR codes) as well as in augmented reality (ARTag, [16]) and camera-calibration (CALTag, [17]) applications.”