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Advanced technologies for beam’s eye view imaging
Published in Ross I. Berbeco, Beam’s Eye View Imaging in Radiation Oncology, 2017
Several kV-based BEV imaging approaches have been proposed (Figure 12.12). A prototype ARTISTE system (Siemens) was developed comprising an in-line source-detector pair (Oelfke et al. 2006). A retractable aSi flat-panel detector situated underneath the treatment head was irradiated by a kV source situated at position nominally 180° away underneath the retractable (MV) EPID. Using phantom studies, it was demonstrated that the imaging framework was capable of automatically detecting fiducial marker positions during a simulated treatment session during which the kV imager was heavily irradiated from its backside by the treatment beam (Fast et al. 2012a). In another approach designed for the ARTISTE, Maltz et al. (2009) proposed using a compact multiple source X-ray tube surrounding the treatment head to produce a tomosynthesis image. The electron sources within the tube are realized using cold cathode carbon nanotube technology and the X-ray images are captured by the same EPID employed for portal imaging. A related approach (Tumotrak, Telesecurity Inc) has been proposed by Partain et al. (2015) where, instead of using fixed nanotube X-ray source technology, a scanning electron beam is utilized for X-ray generation.
Three-dimensional CityGML building models in mobile augmented reality: a smartphone-based pose tracking system
Published in International Journal of Digital Earth, 2021
Christoph Blut, Jörg Blankenbach
To maintain the flexibility of device-based pose tracking, cameras have found an increasing application for realizing optical pose tracking. For instance, White and Feiner (2009) include 6 degree of freedom optical marker tracking in SiteLens, for displaying virtual 3D building models. The fiducial markers are distributed across the environment and are captured by the mobile camera to provide known reference points for correcting the device pose. But like stationary external tracking systems, marker tracking also requires preparing the environment in advance, limiting the versatility of the AR systems. Therefore, efforts have been made to integrate existing physical objects, such as buildings, referred to as natural features, in optical pose tracking. Vacchetti, Lepetit, and Fua (2004), Wuest, Vial, and Stricker (2005), Reitmayr and Drummond (2006), Lima et al. (2010), Choi and Christensen (2012) and Petit, Marchand, and Kanani (2013) show virtual 3D model-based solutions using edge-matching methods. The defining edges of the 3D models are utilized to search for corresponding 2D edges of the physical objects in camera images and matched with these to derive a pose with the Perspective-n-Point (PnP) algorithm. Reitmayr and Drummond (2006) use their system to overlay virtual wireframe models over physical buildings. The drawbacks of these approaches are that the objects must be captured continuously by the camera to estimate poses, restricting mobility, and that, typically, prepared wireframe models are required.
Future vehicles: the effect of seat configuration on posture and quality of conversation
Published in Ergonomics, 2019
Iolanda Fiorillo, Silvana Piro, Shabila Anjani, Maxim Smulders, Yu Song, Alessandro Naddeo, Peter Vink
In computer vision, design fiducial markers are often used for tracking particular landmarks (Hung, Witana, and Goonetilleke 2004). Those designs often allow the real-time calculation of the position and the orientation of the markers based on a frame(s) of the video stream with limited computing power (Avola et al. 2016). By attaching such a marker(s), e.g. an ArUco Marker (Garrido-Jurado et al. 2014; Mondéjar-Guerra et al. 2018), to a particular (anatomical) landmark(s) of a subject, postures of the subject, as well as the motion, can be estimated based on 3D position, orientation and trajectory of this landmark(s).