727 research outputs found
Acoustic Space Learning for Sound Source Separation and Localization on Binaural Manifolds
In this paper we address the problems of modeling the acoustic space
generated by a full-spectrum sound source and of using the learned model for
the localization and separation of multiple sources that simultaneously emit
sparse-spectrum sounds. We lay theoretical and methodological grounds in order
to introduce the binaural manifold paradigm. We perform an in-depth study of
the latent low-dimensional structure of the high-dimensional interaural
spectral data, based on a corpus recorded with a human-like audiomotor robot
head. A non-linear dimensionality reduction technique is used to show that
these data lie on a two-dimensional (2D) smooth manifold parameterized by the
motor states of the listener, or equivalently, the sound source directions. We
propose a probabilistic piecewise affine mapping model (PPAM) specifically
designed to deal with high-dimensional data exhibiting an intrinsic piecewise
linear structure. We derive a closed-form expectation-maximization (EM)
procedure for estimating the model parameters, followed by Bayes inversion for
obtaining the full posterior density function of a sound source direction. We
extend this solution to deal with missing data and redundancy in real world
spectrograms, and hence for 2D localization of natural sound sources such as
speech. We further generalize the model to the challenging case of multiple
sound sources and we propose a variational EM framework. The associated
algorithm, referred to as variational EM for source separation and localization
(VESSL) yields a Bayesian estimation of the 2D locations and time-frequency
masks of all the sources. Comparisons of the proposed approach with several
existing methods reveal that the combination of acoustic-space learning with
Bayesian inference enables our method to outperform state-of-the-art methods.Comment: 19 pages, 9 figures, 3 table
Dense Super-Resolution Imaging of Molecular Orientation via Joint Sparse Basis Deconvolution and Spatial Pooling
In single-molecule super-resolution microscopy, engineered point-spread
functions (PSFs) are designed to efficiently encode new molecular properties,
such as 3D orientation, into complex spatial features captured by a camera. To
fully benefit from their optimality, algorithms must estimate multi-dimensional
parameters such as molecular position and orientation in the presence of PSF
overlap and model-experiment mismatches. Here, we present a novel joint sparse
deconvolution algorithm based on the decomposition of fluorescence images into
six basis images that characterize molecular orientation. The proposed
algorithm exploits a group-sparsity structure across these basis images and
applies a pooling strategy on corresponding spatial features for robust
simultaneous estimates of the number, brightness, 2D position, and 3D
orientation of fluorescent molecules. We demonstrate this method by imaging DNA
transiently labeled with the intercalating dye YOYO-1. Imaging the position and
orientation of each molecule reveals orientational order and disorder within
DNA with nanoscale spatial precision.Comment: Copyright 2019 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other uses, in any current or
future media, including reprinting/republishing this material for advertising
or promotional purposes, creating new collective works, for resale or
redistribution to servers or lists, or reuse of any copyrighted component of
this work in other work
Deep-STORM: super-resolution single-molecule microscopy by deep learning
We present an ultra-fast, precise, parameter-free method, which we term
Deep-STORM, for obtaining super-resolution images from stochastically-blinking
emitters, such as fluorescent molecules used for localization microscopy.
Deep-STORM uses a deep convolutional neural network that can be trained on
simulated data or experimental measurements, both of which are demonstrated.
The method achieves state-of-the-art resolution under challenging
signal-to-noise conditions and high emitter densities, and is significantly
faster than existing approaches. Additionally, no prior information on the
shape of the underlying structure is required, making the method applicable to
any blinking data-set. We validate our approach by super-resolution image
reconstruction of simulated and experimentally obtained data.Comment: 7 pages, added code download reference and DOI for the journal
versio
Learned SPARCOM: Unfolded Deep Super-Resolution Microscopy
The use of photo-activated fluorescent molecules to create long sequences of
low emitter-density diffraction-limited images enables high-precision emitter
localization, but at the cost of low temporal resolution. We suggest combining
SPARCOM, a recent high-performing classical method, with model-based deep
learning, using the algorithm unfolding approach, to design a compact neural
network incorporating domain knowledge. Our results show that we can obtain
super-resolution imaging from a small number of high emitter density frames
without knowledge of the optical system and across different test sets using
the proposed learned SPARCOM (LSPARCOM) network. We believe LSPARCOM can pave
the way to interpretable, efficient live-cell imaging in many settings, and
find broad use in single-molecule localization microscopy of biological
structures
Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mapping
Modern 3D laser-range scanners have a high data rate, making online
simultaneous localization and mapping (SLAM) computationally challenging.
Recursive state estimation techniques are efficient but commit to a state
estimate immediately after a new scan is made, which may lead to misalignments
of measurements. We present a 3D SLAM approach that allows for refining
alignments during online mapping. Our method is based on efficient local
mapping and a hierarchical optimization back-end. Measurements of a 3D laser
scanner are aggregated in local multiresolution maps by means of surfel-based
registration. The local maps are used in a multi-level graph for allocentric
mapping and localization. In order to incorporate corrections when refining the
alignment, the individual 3D scans in the local map are modeled as a sub-graph
and graph optimization is performed to account for drift and misalignments in
the local maps. Furthermore, in each sub-graph, a continuous-time
representation of the sensor trajectory allows to correct measurements between
scan poses. We evaluate our approach in multiple experiments by showing
qualitative results. Furthermore, we quantify the map quality by an
entropy-based measure.Comment: In: Proceedings of the International Conference on Robotics and
Automation (ICRA) 201
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