11,306 research outputs found
Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures
Here, using an integrative experimental and computational approach, Imle et al. show how cell motility and density affect HIV cell-associated transmission in a three-dimensional tissue-like culture system of CD4+ T cells and collagen, and how different collagen matrices restrict infection by cell-free virions
Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach
This paper proposes a probabilistic approach for the detection and the
tracking of particles in fluorescent time-lapse imaging. In the presence of a
very noised and poor-quality data, particles and trajectories can be
characterized by an a contrario model, that estimates the probability of
observing the structures of interest in random data. This approach, first
introduced in the modeling of human visual perception and then successfully
applied in many image processing tasks, leads to algorithms that neither
require a previous learning stage, nor a tedious parameter tuning and are very
robust to noise. Comparative evaluations against a well-established baseline
show that the proposed approach outperforms the state of the art.Comment: Published in Journal of Machine Vision and Application
Precise 3D track reconstruction algorithm for the ICARUS T600 liquid argon time projection chamber detector
Liquid Argon Time Projection Chamber (LAr TPC) detectors offer charged
particle imaging capability with remarkable spatial resolution. Precise event
reconstruction procedures are critical in order to fully exploit the potential
of this technology. In this paper we present a new, general approach of
three-dimensional reconstruction for the LAr TPC with a practical application
to track reconstruction. The efficiency of the method is evaluated on a sample
of simulated tracks. We present also the application of the method to the
analysis of real data tracks collected during the ICARUS T600 detector
operation with the CNGS neutrino beam.Comment: Submitted to Advances in High Energy Physic
Rotationally Invariant Image Representation for Viewing Direction Classification in Cryo-EM
We introduce a new rotationally invariant viewing angle classification method
for identifying, among a large number of Cryo-EM projection images, similar
views without prior knowledge of the molecule. Our rotationally invariant
features are based on the bispectrum. Each image is denoised and compressed
using steerable principal component analysis (PCA) such that rotating an image
is equivalent to phase shifting the expansion coefficients. Thus we are able to
extend the theory of bispectrum of 1D periodic signals to 2D images. The
randomized PCA algorithm is then used to efficiently reduce the dimensionality
of the bispectrum coefficients, enabling fast computation of the similarity
between any pair of images. The nearest neighbors provide an initial
classification of similar viewing angles. In this way, rotational alignment is
only performed for images with their nearest neighbors. The initial nearest
neighbor classification and alignment are further improved by a new
classification method called vector diffusion maps. Our pipeline for viewing
angle classification and alignment is experimentally shown to be faster and
more accurate than reference-free alignment with rotationally invariant K-means
clustering, MSA/MRA 2D classification, and their modern approximations
A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber
We have developed a convolutional neural network (CNN) that can make a
pixel-level prediction of objects in image data recorded by a liquid argon time
projection chamber (LArTPC) for the first time. We describe the network design,
training techniques, and software tools developed to train this network. The
goal of this work is to develop a complete deep neural network based data
reconstruction chain for the MicroBooNE detector. We show the first
demonstration of a network's validity on real LArTPC data using MicroBooNE
collection plane images. The demonstration is performed for stopping muon and a
charged current neutral pion data samples
Factored particle filtering with dependent and constrained partition dynamics for tracking deformable objects
In particle filtering, dimensionality of the state space can be reduced by tracking control (or feature) points as independent objects, which are traditionally named as partitions. Two critical decisions have to be made in implementation of reduced state-space dimensionality. First is how to construct a dynamic (transition) model for partitions that are inherently dependent. Second critical decision is how to filter partition states such that a viable and likely object state is achieved. In this study, we present a correlation-based transition model and a proposal function that incorporate partition dependency in particle filtering in a computationally tractable manner. We test our algorithm on challenging examples of occlusion, clutter and drastic changes in relative speeds of partitions. Our successful results with as low as 10 particles per partition indicate that the proposed algorithm is both robust and efficient.This research is part of project "Expression Recognition based on Facial Anatomy", grant number 109E061, supported by The Support Programme for Scientific and Technological Research Projects of The Scientific and Technological Research Council of Turkey (TUBITAK). In comparative evaluation of the tracking algorithms we utilized the SPOT tracking code that was made publicly available by researchers Lu Zhang and Laurens van der Maaten. A special thanks to Fish Species who generously provided the high definition aquarium videos used in our experiments (http://www.fish-species.org.uk)Publisher's VersionAuthor Post Prin
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