5,237 research outputs found
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
Computational illumination for high-speed in vitro Fourier ptychographic microscopy
We demonstrate a new computational illumination technique that achieves large
space-bandwidth-time product, for quantitative phase imaging of unstained live
samples in vitro. Microscope lenses can have either large field of view (FOV)
or high resolution, not both. Fourier ptychographic microscopy (FPM) is a new
computational imaging technique that circumvents this limit by fusing
information from multiple images taken with different illumination angles. The
result is a gigapixel-scale image having both wide FOV and high resolution,
i.e. large space-bandwidth product (SBP). FPM has enormous potential for
revolutionizing microscopy and has already found application in digital
pathology. However, it suffers from long acquisition times (on the order of
minutes), limiting throughput. Faster capture times would not only improve
imaging speed, but also allow studies of live samples, where motion artifacts
degrade results. In contrast to fixed (e.g. pathology) slides, live samples are
continuously evolving at various spatial and temporal scales. Here, we present
a new source coding scheme, along with real-time hardware control, to achieve
0.8 NA resolution across a 4x FOV with sub-second capture times. We propose an
improved algorithm and new initialization scheme, which allow robust phase
reconstruction over long time-lapse experiments. We present the first FPM
results for both growing and confluent in vitro cell cultures, capturing videos
of subcellular dynamical phenomena in popular cell lines undergoing division
and migration. Our method opens up FPM to applications with live samples, for
observing rare events in both space and time
Tensor approximation in visualization and graphics
In this course, we will introduce the basic concepts of tensor approximation (TA) – a higher-order generalization of the SVD and PCA methods – as well as its applications to visual data representation, analysis and visualization, and bring the TA framework closer to visualization and computer graphics researchers and practitioners. The course will cover the theoretical background of TA methods, their properties and how to compute them, as well as practical applications of TA methods in visualization and computer graphics contexts. In a first theoretical part, the attendees will be instructed on the necessary mathematical background of TA methods to learn the basics skills of using and applying these new tools in the context of the representation of large multidimensional visual data. Specific and very noteworthy features of the TA framework are highlighted which can effectively be exploited for spatio-temporal multidimensional data representation and visualization purposes. In two application oriented sessions, compact TA data representation in scientific visualization and computer graphics as well as decomposition and reconstruction algorithms will be demonstrated. At the end of the course, the participants will have a good basic knowledge of TA methods along with a practical understanding of its potential application in visualization and graphics related projects
Secretory vesicles are preferentially targeted to areas of low molecular SNARE density
Intercellular communication is commonly mediated by the regulated fusion, or exocytosis, of vesicles with the cell surface. SNARE (soluble N-ethymaleimide sensitive factor attachment protein receptor) proteins are the catalytic core of the secretory machinery, driving vesicle and plasma membrane merger. Plasma membrane SNAREs (tSNAREs) are proposed to reside in dense clusters containing many molecules, thus providing a concentrated reservoir to promote membrane fusion. However, biophysical experiments suggest that a small number of SNAREs are sufficient to drive a single fusion event. Here we show, using molecular imaging, that the majority of tSNARE molecules are spatially separated from secretory vesicles. Furthermore, the motilities of the individual tSNAREs are constrained in membrane micro-domains, maintaining a non-random molecular distribution and limiting the maximum number of molecules encountered by secretory vesicles. Together our results provide a new model for the molecular mechanism of regulated exocytosis and demonstrate the exquisite organization of the plasma membrane at the level of individual molecular machines
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