110 research outputs found
Interactive Camera Network Design using a Virtual Reality Interface
Traditional literature on camera network design focuses on constructing
automated algorithms. These require problem specific input from experts in
order to produce their output. The nature of the required input is highly
unintuitive leading to an unpractical workflow for human operators. In this
work we focus on developing a virtual reality user interface allowing human
operators to manually design camera networks in an intuitive manner. From real
world practical examples we conclude that the camera networks designed using
this interface are highly competitive with, or superior to those generated by
automated algorithms, but the associated workflow is much more intuitive and
simple. The competitiveness of the human-generated camera networks is
remarkable because the structure of the optimization problem is a well known
combinatorial NP-hard problem. These results indicate that human operators can
be used in challenging geometrical combinatorial optimization problems given an
intuitive visualization of the problem.Comment: 11 pages, 8 figure
One-shot Feature-Preserving Point Cloud Simplification with Gaussian Processes on Riemannian Manifolds
The processing, storage and transmission of large-scale point clouds is an
ongoing challenge in the computer vision community which hinders progress in
the application of 3D models to real-world settings, such as autonomous
driving, virtual reality and remote sensing. We propose a novel, one-shot point
cloud simplification method which preserves both the salient structural
features and the overall shape of a point cloud without any prior surface
reconstruction step. Our method employs Gaussian processes with kernels defined
on Riemannian manifolds, allowing us to model the surface variation function
across any given point cloud. A simplified version of the original cloud is
obtained by sequentially selecting points using a greedy sparsification scheme.
The selection criterion used for this scheme ensures that the simplified cloud
best represents the surface variation of the original point cloud. We evaluate
our method on several benchmark datasets, compare it to a range of existing
methods and show that our method is competitive both in terms of empirical
performance and computational efficiency.Comment: 10 pages, 5 figure
How to turn your camera into a perfect pinhole model
Camera calibration is a first and fundamental step in various computer vision
applications. Despite being an active field of research, Zhang's method remains
widely used for camera calibration due to its implementation in popular
toolboxes. However, this method initially assumes a pinhole model with
oversimplified distortion models. In this work, we propose a novel approach
that involves a pre-processing step to remove distortions from images by means
of Gaussian processes. Our method does not need to assume any distortion model
and can be applied to severely warped images, even in the case of multiple
distortion sources, e.g., a fisheye image of a curved mirror reflection. The
Gaussian processes capture all distortions and camera imperfections, resulting
in virtual images as though taken by an ideal pinhole camera with square
pixels. Furthermore, this ideal GP-camera only needs one image of a square grid
calibration pattern. This model allows for a serious upgrade of many algorithms
and applications that are designed in a pure projective geometry setting but
with a performance that is very sensitive to nonlinear lens distortions. We
demonstrate the effectiveness of our method by simplifying Zhang's calibration
method, reducing the number of parameters and getting rid of the distortion
parameters and iterative optimization. We validate by means of synthetic data
and real world images. The contributions of this work include the construction
of a virtual ideal pinhole camera using Gaussian processes, a simplified
calibration method and lens distortion removal.Comment: 15 pages, 3 figures, conference CIAR
Assessment and treatment of visuospatial neglect using active learning with Gaussian processes regression
Visuospatial neglect is a disorder characterised by impaired awareness for
visual stimuli located in regions of space and frames of reference. It is often
associated with stroke. Patients can struggle with all aspects of daily living
and community participation. Assessment methods are limited and show several
shortcomings, considering they are mainly performed on paper and do not
implement the complexity of daily life. Similarly, treatment options are sparse
and often show only small improvements. We present an artificial intelligence
solution designed to accurately assess a patient's visuospatial neglect in a
three-dimensional setting. We implement an active learning method based on
Gaussian process regression to reduce the effort it takes a patient to undergo
an assessment. Furthermore, we describe how this model can be utilised in
patient oriented treatment and how this opens the way to gamification,
tele-rehabilitation and personalised healthcare, providing a promising avenue
for improving patient engagement and rehabilitation outcomes. To validate our
assessment module, we conducted clinical trials involving patients in a
real-world setting. We compared the results obtained using our AI-based
assessment with the widely used conventional visuospatial neglect tests
currently employed in clinical practice. The validation process serves to
establish the accuracy and reliability of our model, confirming its potential
as a valuable tool for diagnosing and monitoring visuospatial neglect. Our VR
application proves to be more sensitive, while intra-rater reliability remains
high
Relative centers of motion, implicit bars and dead-center positions for planar mechanisms
AbstractThis paper characterizes the dead-center positions of a planar mechanism in terms of implicit bars, that can be described in their turn in terms of relative motion centers. Consequently, a graphical procedure for finding motion centers leads to a geometric description for dead-point positions. We give a survey of existing geometric constructions for motion centers, and we illustrate a new technique that makes use of the “Baracs construction”
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