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Artificial Intelligence: Revolution, Definitions, Ethics, and Foundation
Published in Puneet Kumar, Vinod Kumar Jain, Dharminder Kumar, Artificial Intelligence and Global Society, 2021
Visualizing objects using human power can be limited, so AI can be utilized to sense, analyze, understand, and aid several types of decision making. Using computer vision, AI can be used for scene reconstruction (filling the missing bits of an image or video), event detection (detecting a particular signal in the image or video), object recognition, 3D pose estimation, motion estimation, and image restoration.
A tool-free neuronavigation method based on single-view hand tracking
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023
Fryderyk Victor Kögl, Étienne Léger, Nazim Haouchine, Erickson Torio, Parikshit Juvekar, Nassir Navab, Tina Kapur, Steve Pieper, Alexandra Golby, Sarah Frisken
Tracking the fingers allows surgeons to interact in a naturally intuitive way with the system to identify the anatomical landmarks used for the registration. Hand tracking has been an active research topic that attained a high level of accuracy and robustness thanks to the successful integration of machine learning techniques. However, most of the existing methods provide only a 2.5D pose estimation using a monocular RGB camera, i.e. depth is estimated relative to the centre of the hand, by opposition to a real 3D pose, where the depth would be defined in absolute terms, relative to the scene or the camera. In order to obtain a true 3D pose estimation, w.r.t the camera position, specialised hardware, e.g. depth sensors are required, which is not available in our configuration. In the following, we describe our method to obtain a full 3D hand pose using solely a commodity monocular RGB camera.