6,236 research outputs found
GPGPU for track finding in High Energy Physics
The LHC experiments are designed to detect large amount of physics events
produced with a very high rate. Considering the future upgrades, the data
acquisition rate will become even higher and new computing paradigms must be
adopted for fast data-processing: General Purpose Graphics Processing Units
(GPGPU) is a novel approach based on massive parallel computing. The intense
computation power provided by Graphics Processing Units (GPU) is expected to
reduce the computation time and to speed-up the low-latency applications used
for fast decision taking. In particular, this approach could be hence used for
high-level triggering in very complex environments, like the typical inner
tracking systems of the multi-purpose experiments at LHC, where a large number
of charged particle tracks will be produced with the luminosity upgrade. In
this article we discuss a track pattern recognition algorithm based on the
Hough Transform, where a parallel approach is expected to reduce dramatically
the execution time.Comment: 6 pages, 4 figures, proceedings prepared for GPU-HEP 2014 conference,
submitted to DESY-PROC-201
A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds
This paper proposes a segmentation-free, automatic and efficient procedure to
detect general geometric quadric forms in point clouds, where clutter and
occlusions are inevitable. Our everyday world is dominated by man-made objects
which are designed using 3D primitives (such as planes, cones, spheres,
cylinders, etc.). These objects are also omnipresent in industrial
environments. This gives rise to the possibility of abstracting 3D scenes
through primitives, thereby positions these geometric forms as an integral part
of perception and high level 3D scene understanding.
As opposed to state-of-the-art, where a tailored algorithm treats each
primitive type separately, we propose to encapsulate all types in a single
robust detection procedure. At the center of our approach lies a closed form 3D
quadric fit, operating in both primal & dual spaces and requiring as low as 4
oriented-points. Around this fit, we design a novel, local null-space voting
strategy to reduce the 4-point case to 3. Voting is coupled with the famous
RANSAC and makes our algorithm orders of magnitude faster than its conventional
counterparts. This is the first method capable of performing a generic
cross-type multi-object primitive detection in difficult scenes. Results on
synthetic and real datasets support the validity of our method.Comment: Accepted for publication at CVPR 201
Efficient Spatially Adaptive Convolution and Correlation
Fast methods for convolution and correlation underlie a variety of
applications in computer vision and graphics, including efficient filtering,
analysis, and simulation. However, standard convolution and correlation are
inherently limited to fixed filters: spatial adaptation is impossible without
sacrificing efficient computation. In early work, Freeman and Adelson have
shown how steerable filters can address this limitation, providing a way for
rotating the filter as it is passed over the signal. In this work, we provide a
general, representation-theoretic, framework that allows for spatially varying
linear transformations to be applied to the filter. This framework allows for
efficient implementation of extended convolution and correlation for
transformation groups such as rotation (in 2D and 3D) and scale, and provides a
new interpretation for previous methods including steerable filters and the
generalized Hough transform. We present applications to pattern matching, image
feature description, vector field visualization, and adaptive image filtering
Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images
Iris centre localization in low-resolution visible images is a challenging
problem in computer vision community due to noise, shadows, occlusions, pose
variations, eye blinks, etc. This paper proposes an efficient method for
determining iris centre in low-resolution images in the visible spectrum. Even
low-cost consumer-grade webcams can be used for gaze tracking without any
additional hardware. A two-stage algorithm is proposed for iris centre
localization. The proposed method uses geometrical characteristics of the eye.
In the first stage, a fast convolution based approach is used for obtaining the
coarse location of iris centre (IC). The IC location is further refined in the
second stage using boundary tracing and ellipse fitting. The algorithm has been
evaluated in public databases like BioID, Gi4E and is found to outperform the
state of the art methods.Comment: 12 pages, 10 figures, IET Computer Vision, 201
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