7,481 research outputs found
Deep Learning Enabled Real Time Speckle Recognition and Hyperspectral Imaging using a Multimode Fiber Array
We demonstrate the use of deep learning for fast spectral deconstruction of
speckle patterns. The artificial neural network can be effectively trained
using numerically constructed multispectral datasets taken from a measured
spectral transmission matrix. Optimized neural networks trained on these
datasets achieve reliable reconstruction of both discrete and continuous
spectra from a monochromatic camera image. Deep learning is compared to
analytical inversion methods as well as to a compressive sensing algorithm and
shows favourable characteristics both in the oversampling and in the sparse
undersampling (compressive) regimes. The deep learning approach offers
significant advantages in robustness to drift or noise and in reconstruction
speed. In a proof-of-principle demonstrator we achieve real time recovery of
hyperspectral information using a multi-core, multi-mode fiber array as a
random scattering medium.Comment: 12 pages, 6 figures + Appendix of 5 pages and 5 figure
ASP Vision: Optically Computing the First Layer of Convolutional Neural Networks using Angle Sensitive Pixels
Deep learning using convolutional neural networks (CNNs) is quickly becoming
the state-of-the-art for challenging computer vision applications. However,
deep learning's power consumption and bandwidth requirements currently limit
its application in embedded and mobile systems with tight energy budgets. In
this paper, we explore the energy savings of optically computing the first
layer of CNNs. To do so, we utilize bio-inspired Angle Sensitive Pixels (ASPs),
custom CMOS diffractive image sensors which act similar to Gabor filter banks
in the V1 layer of the human visual cortex. ASPs replace both image sensing and
the first layer of a conventional CNN by directly performing optical edge
filtering, saving sensing energy, data bandwidth, and CNN FLOPS to compute. Our
experimental results (both on synthetic data and a hardware prototype) for a
variety of vision tasks such as digit recognition, object recognition, and face
identification demonstrate using ASPs while achieving similar performance
compared to traditional deep learning pipelines.Comment: Presented in CVPR 2016 (oral), 10 pages, 12 figures. This new version
corrects the comparison between imaging power for ASPs and a regular image
senso
ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Random Measurements
The goal of this paper is to present a non-iterative and more importantly an
extremely fast algorithm to reconstruct images from compressively sensed (CS)
random measurements. To this end, we propose a novel convolutional neural
network (CNN) architecture which takes in CS measurements of an image as input
and outputs an intermediate reconstruction. We call this network, ReconNet. The
intermediate reconstruction is fed into an off-the-shelf denoiser to obtain the
final reconstructed image. On a standard dataset of images we show significant
improvements in reconstruction results (both in terms of PSNR and time
complexity) over state-of-the-art iterative CS reconstruction algorithms at
various measurement rates. Further, through qualitative experiments on real
data collected using our block single pixel camera (SPC), we show that our
network is highly robust to sensor noise and can recover visually better
quality images than competitive algorithms at extremely low sensing rates of
0.1 and 0.04. To demonstrate that our algorithm can recover semantically
informative images even at a low measurement rate of 0.01, we present a very
robust proof of concept real-time visual tracking application.Comment: Accepted at IEEE International Conference on Computer Vision and
Pattern Recognition (CVPR), 201
Deep Ptych: Subsampled Fourier Ptychography using Generative Priors
This paper proposes a novel framework to regularize the highly ill-posed and
non-linear Fourier ptychography problem using generative models. We demonstrate
experimentally that our proposed algorithm, Deep Ptych, outperforms the
existing Fourier ptychography techniques, in terms of quality of reconstruction
and robustness against noise, using far fewer samples. We further modify the
proposed approach to allow the generative model to explore solutions outside
the range, leading to improved performance
On some common compressive sensing recovery algorithms and applications - Review paper
Compressive Sensing, as an emerging technique in signal processing is
reviewed in this paper together with its common applications. As an alternative
to the traditional signal sampling, Compressive Sensing allows a new
acquisition strategy with significantly reduced number of samples needed for
accurate signal reconstruction. The basic ideas and motivation behind this
approach are provided in the theoretical part of the paper. The commonly used
algorithms for missing data reconstruction are presented. The Compressive
Sensing applications have gained significant attention leading to an intensive
growth of signal processing possibilities. Hence, some of the existing
practical applications assuming different types of signals in real-world
scenarios are described and analyzed as well.Comment: submitted to Facta Universitatis Scientific Journal, Series:
Electronics and Energetics, March 201
Robust event-stream pattern tracking based on correlative filter
Object tracking based on retina-inspired and event-based dynamic vision
sensor (DVS) is challenging for the noise events, rapid change of event-stream
shape, chaos of complex background textures, and occlusion. To address these
challenges, this paper presents a robust event-stream pattern tracking method
based on correlative filter mechanism. In the proposed method, rate coding is
used to encode the event-stream object in each segment. Feature representations
from hierarchical convolutional layers of a deep convolutional neural network
(CNN) are used to represent the appearance of the rate encoded event-stream
object. The results prove that our method not only achieves good tracking
performance in many complicated scenes with noise events, complex background
textures, occlusion, and intersected trajectories, but also is robust to
variable scale, variable pose, and non-rigid deformations. In addition, this
correlative filter based event-stream tracking has the advantage of high speed.
The proposed approach will promote the potential applications of these
event-based vision sensors in self-driving, robots and many other high-speed
scenes
Rank Minimization for Snapshot Compressive Imaging
Snapshot compressive imaging (SCI) refers to compressive imaging systems
where multiple frames are mapped into a single measurement, with video
compressive imaging and hyperspectral compressive imaging as two representative
applications. Though exciting results of high-speed videos and hyperspectral
images have been demonstrated, the poor reconstruction quality precludes SCI
from wide applications.This paper aims to boost the reconstruction quality of
SCI via exploiting the high-dimensional structure in the desired signal. We
build a joint model to integrate the nonlocal self-similarity of
video/hyperspectral frames and the rank minimization approach with the SCI
sensing process. Following this, an alternating minimization algorithm is
developed to solve this non-convex problem. We further investigate the special
structure of the sampling process in SCI to tackle the computational workload
and memory issues in SCI reconstruction. Both simulation and real data
(captured by four different SCI cameras) results demonstrate that our proposed
algorithm leads to significant improvements compared with current
state-of-the-art algorithms. We hope our results will encourage the researchers
and engineers to pursue further in compressive imaging for real applications.Comment: 18 pages, 21 figures, and 2 tables. Code available at
https://github.com/liuyang12/DeSC
Cells exploit a phase transition to mechanically remodel the fibrous extracellular matrix
Through mechanical forces, biological cells remodel the surrounding collagen network, generating striking deformation patterns. Tethers—tracts of high densification and fibre alignment—form between cells, thinner bands emanate from cell clusters. While tethers facilitate cell migration and communication, how they form is unclear. Combining modelling, simulation and experiment, we show that tether formation is a densification phase transition of the extracellular matrix, caused by buckling instability of network fibres under cell-induced compression, featuring unexpected similarities with martensitic microstructures. Multiscale averaging yields a two-phase, bistable continuum energy landscape for fibrous collagen, with a densified/aligned second phase. Simulations predict strain discontinuities between the undensified and densified phase, which localizes within tethers as experimentally observed. In our experiments, active particles induce similar localized patterns as cells. This shows how cells exploit an instability to mechanically remodel the extracellular matrix simply by contracting, thereby facilitating mechanosensing, invasion and metastasis
Affect Sensing on Smartphone - Possibilities of Understanding Cognitive Decline in Aging Population
Due to increasing sensing capacity, smartphones offer unprecedented
opportunity to monitor human health. Affect sensing is one such essential
monitoring that can be achieved on smartphones. Information about affect can be
useful for many modern applications. In particular, it can be potentially used
for understanding cognitive decline in aging population. In this paper we
present an overview of the existing literature that offer affect sensing on
smartphone platform. Most importantly, we present the challenges that need to
be addressed to make affect sensing on smartphone a reality.Comment: This paper has been withdrawn due to some conceptual erro
Flexible and tunable silicon photonic circuits on plastic substrates
Flexible microelectronics has shown tremendous promise in a broad spectrum of
applications, especially those that cannot be addressed by conventional
microelectronics in rigid materials and constructions1-3. These unconventional
yet important applications range from flexible consumer electronics to
conformal sensor arrays and biomedical devices. A recent successful paradigm
shift in implementing flexible electronics is to physically transfer and bond
highly integrated devices made in high-quality, crystalline semiconductor
materials on to plastic materials4-8. Here we demonstrate a flexible form of
silicon photonics on plastic substrates using the transfer-and-bond fabrication
method. Photonic circuits including interferometers and resonators have been
transferred onto flexible plastic substrates with preserved functionalities and
performance. By mechanically deforming the flexible substrates, the optical
characteristics of the devices can be tuned reversibly over a remarkably large
range. The demonstration of the new flexible photonic system based on the
silicon-on-plastic (SOP) material platform could open the door to a plethora of
novel applications, including tunable photonics, optomechanical sensors and
bio-mechanical and bio-photonic probes.Comment: Part of this work was presented at 2012 CLEO conference on May 8th,
201
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