51,180 research outputs found
Parameterized Construction of Program Representations for Sparse Dataflow Analyses
Data-flow analyses usually associate information with control flow regions.
Informally, if these regions are too small, like a point between two
consecutive statements, we call the analysis dense. On the other hand, if these
regions include many such points, then we call it sparse. This paper presents a
systematic method to build program representations that support sparse
analyses. To pave the way to this framework we clarify the bibliography about
well-known intermediate program representations. We show that our approach, up
to parameter choice, subsumes many of these representations, such as the SSA,
SSI and e-SSA forms. In particular, our algorithms are faster, simpler and more
frugal than the previous techniques used to construct SSI - Static Single
Information - form programs. We produce intermediate representations isomorphic
to Choi et al.'s Sparse Evaluation Graphs (SEG) for the family of data-flow
problems that can be partitioned per variables. However, contrary to SEGs, we
can handle - sparsely - problems that are not in this family
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
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