4,573 research outputs found
Synthetic retina for AER systems development
Neuromorphic engineering tries to mimic biology in
information processing. Address-Event Representation (AER) is
a neuromorphic communication protocol for spiking neurons
between different layers. AER bio-inspired image sensor are
called “retina”. This kind of sensors measure visual information
not based on frames from real life and generates corresponding
events. In this paper we provide an alternative, based on cheap
FPGA, to this image sensors that takes images provided by an
analog video source (video composite signal), digitalizes it and
generates AER streams for testing purposes.Junta de Andalucía P06-TIC-01417Ministerio de Educación y Ciencia TEC2006-11730-C03-0
Depth Fields: Extending Light Field Techniques to Time-of-Flight Imaging
A variety of techniques such as light field, structured illumination, and
time-of-flight (TOF) are commonly used for depth acquisition in consumer
imaging, robotics and many other applications. Unfortunately, each technique
suffers from its individual limitations preventing robust depth sensing. In
this paper, we explore the strengths and weaknesses of combining light field
and time-of-flight imaging, particularly the feasibility of an on-chip
implementation as a single hybrid depth sensor. We refer to this combination as
depth field imaging. Depth fields combine light field advantages such as
synthetic aperture refocusing with TOF imaging advantages such as high depth
resolution and coded signal processing to resolve multipath interference. We
show applications including synthesizing virtual apertures for TOF imaging,
improved depth mapping through partial and scattering occluders, and single
frequency TOF phase unwrapping. Utilizing space, angle, and temporal coding,
depth fields can improve depth sensing in the wild and generate new insights
into the dimensions of light's plenoptic function.Comment: 9 pages, 8 figures, Accepted to 3DV 201
Temporal shape super-resolution by intra-frame motion encoding using high-fps structured light
One of the solutions of depth imaging of moving scene is to project a static
pattern on the object and use just a single image for reconstruction. However,
if the motion of the object is too fast with respect to the exposure time of
the image sensor, patterns on the captured image are blurred and reconstruction
fails. In this paper, we impose multiple projection patterns into each single
captured image to realize temporal super resolution of the depth image
sequences. With our method, multiple patterns are projected onto the object
with higher fps than possible with a camera. In this case, the observed pattern
varies depending on the depth and motion of the object, so we can extract
temporal information of the scene from each single image. The decoding process
is realized using a learning-based approach where no geometric calibration is
needed. Experiments confirm the effectiveness of our method where sequential
shapes are reconstructed from a single image. Both quantitative evaluations and
comparisons with recent techniques were also conducted.Comment: 9 pages, Published at the International Conference on Computer Vision
(ICCV 2017
A preliminary experiment definition for video landmark acquisition and tracking
Six scientific objectives/experiments were derived which consisted of agriculture/forestry/range resources, land use, geology/mineral resources, water resources, marine resources and environmental surveys. Computer calculations were then made of the spectral radiance signature of each of 25 candidate targets as seen by a satellite sensor system. An imaging system capable of recognizing, acquiring and tracking specific generic type surface features was defined. A preliminary experiment definition and design of a video Landmark Acquisition and Tracking system is given. This device will search a 10-mile swath while orbiting the earth, looking for land/water interfaces such as coastlines and rivers
They See Me Rollin': Inherent Vulnerability of the Rolling Shutter in CMOS Image Sensors
In this paper, we describe how the electronic rolling shutter in CMOS image
sensors can be exploited using a bright, modulated light source (e.g., an
inexpensive, off-the-shelf laser), to inject fine-grained image disruptions. We
demonstrate the attack on seven different CMOS cameras, ranging from cheap IoT
to semi-professional surveillance cameras, to highlight the wide applicability
of the rolling shutter attack. We model the fundamental factors affecting a
rolling shutter attack in an uncontrolled setting. We then perform an
exhaustive evaluation of the attack's effect on the task of object detection,
investigating the effect of attack parameters. We validate our model against
empirical data collected on two separate cameras, showing that by simply using
information from the camera's datasheet the adversary can accurately predict
the injected distortion size and optimize their attack accordingly. We find
that an adversary can hide up to 75% of objects perceived by state-of-the-art
detectors by selecting appropriate attack parameters. We also investigate the
stealthiness of the attack in comparison to a na\"{i}ve camera blinding attack,
showing that common image distortion metrics can not detect the attack
presence. Therefore, we present a new, accurate and lightweight enhancement to
the backbone network of an object detector to recognize rolling shutter
attacks. Overall, our results indicate that rolling shutter attacks can
substantially reduce the performance and reliability of vision-based
intelligent systems.Comment: 15 pages, 15 figure
- …