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Building 3D scene models has been a longstanding goal of computer vision. The great progress in depth sensors brings us one step closer to achieving this in a single shot. However, depth sensors still produce imperfect measurements that are sparse and cont ...
Curvilinear networks are prevalent in nature and span many different scales, ranging from micron-scale neural structures in the brain to petameter-scale dark-matter arbors binding massive galaxy clusters. Reliably reconstructing them in an automated fashio ...
Because standard cameras sample the scene at constant time intervals, they do not provide any information in the blind time between subsequent frames. However, for many high-speed robotic and vision applications, it is crucial to provide high-frequency mea ...
Imaging techniques in radio interferometry often face a significant challenge posed by the large number of antenna signals received, from which the image information needs to be extracted. Beam- forming is envisaged to reduce the rate required for transpor ...
A study has been conducted to review viscoelastic and foam-constitutive models to describe sport ball response to impact with a rigid cylindrical surface. The impact model was developed to simulate a ball-bat collision. Comparisons were made to actual impa ...
Registration algorithms are an essential component of many computer graphics and computer vision systems. With recent technological advances in RGBD sensors (color plus depth), an active area of research is in techniques combining color, geometry, and lear ...
This thesis addresses the problem of recovering the 3-D shape of a deformable object in single images, or image sequences acquired by a monocular video camera, given that a 3-D template shape and a template image of the object are available. While being a ...
Semantically describing the contents of images is one of the classical problems of computer vision. With huge numbers of images being made available daily, there is increasing interest in methods for semantic pixel labelling that exploit large image sets. ...
Complexity is a double-edged sword for learning algorithms when the number of available samples for training in relation to the dimension of the feature space is small. This is because simple models do not sufficiently capture the nuances of the data set, ...
Event cameras are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. They offer significant advantages over standard cameras, namely a very high dynamic range, no motion blur, and a latency in the o ...