4,984 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
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions
Towards High-Frequency Tracking and Fast Edge-Aware Optimization
This dissertation advances the state of the art for AR/VR tracking systems by
increasing the tracking frequency by orders of magnitude and proposes an
efficient algorithm for the problem of edge-aware optimization.
AR/VR is a natural way of interacting with computers, where the physical and
digital worlds coexist. We are on the cusp of a radical change in how humans
perform and interact with computing. Humans are sensitive to small
misalignments between the real and the virtual world, and tracking at
kilo-Hertz frequencies becomes essential. Current vision-based systems fall
short, as their tracking frequency is implicitly limited by the frame-rate of
the camera. This thesis presents a prototype system which can track at orders
of magnitude higher than the state-of-the-art methods using multiple commodity
cameras. The proposed system exploits characteristics of the camera
traditionally considered as flaws, namely rolling shutter and radial
distortion. The experimental evaluation shows the effectiveness of the method
for various degrees of motion.
Furthermore, edge-aware optimization is an indispensable tool in the computer
vision arsenal for accurate filtering of depth-data and image-based rendering,
which is increasingly being used for content creation and geometry processing
for AR/VR. As applications increasingly demand higher resolution and speed,
there exists a need to develop methods that scale accordingly. This
dissertation proposes such an edge-aware optimization framework which is
efficient, accurate, and algorithmically scales well, all of which are much
desirable traits not found jointly in the state of the art. The experiments
show the effectiveness of the framework in a multitude of computer vision tasks
such as computational photography and stereo.Comment: PhD thesi
High-Precision Localization Using Ground Texture
Location-aware applications play an increasingly critical role in everyday
life. However, satellite-based localization (e.g., GPS) has limited accuracy
and can be unusable in dense urban areas and indoors. We introduce an
image-based global localization system that is accurate to a few millimeters
and performs reliable localization both indoors and outside. The key idea is to
capture and index distinctive local keypoints in ground textures. This is based
on the observation that ground textures including wood, carpet, tile, concrete,
and asphalt may look random and homogeneous, but all contain cracks, scratches,
or unique arrangements of fibers. These imperfections are persistent, and can
serve as local features. Our system incorporates a downward-facing camera to
capture the fine texture of the ground, together with an image processing
pipeline that locates the captured texture patch in a compact database
constructed offline. We demonstrate the capability of our system to robustly,
accurately, and quickly locate test images on various types of outdoor and
indoor ground surfaces
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