The small-scale structure of the fluctuating passive scalar field in a turbulent boundary layer

Issued as final reportNational Science Foundatio

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

The contribution of closed loop tracking control of motion platform on laterally induced postural instability of the drivers at SAAM dynamic simulator

This paper explains the effect of a motion platform closed loop control comparing to the static condition for driving simulators on postural instability. The postural instabilities of the participants (N=18, 15 male and 3 female subjects) were measured as lateral displacements of subject body centre of pressure (YCP ) just before and after each driving session via a balance platform. After having completed the experiments, the two-tailed Mann-Whitney U test was applied to analyze the objective data for merely the post-exposure cases. The objective data analysis revealed that the YCP for the dynamic case indicated a significant lower value than the static situation (U(18), p < 0,0001). It can be concluded that the closed loop tracking control of the hexapod platform of the driving simulator (dynamic platform condition) decreased significantly the lateral postural stability compared to the static operation condition. However the two-tailed Mann-Whitney U test showed that no significant difference was obtained between the two conditions in terms of psychophysical perception

Perception Of Visual Speed While Moving

During self-motion, the world normally appears stationary. In part, this may be due to reductions in visual motion signals during self-motion. In 8 experiments, the authors used magnitude estimation to characterize changes in visual speed perception as a result of biomechanical self-motion alone (treadmill walking), physical translation alone (passive transport), and both biomechanical self-motion and physical translation together (walking). Their results show that each factor alone produces subtractive reductions in visual speed but that subtraction is greatest with both factors together, approximating the sum of the 2 separately. The similarity of results for biomechanical and passive self-motion support H. B. Barlow\u27s (1990) inhibition theory of sensory correlation as a mechanism for implementing H. Wallach\u27s (1987) compensation for self-motion. (PsycINFO Database Record (c) 2013 APA, all rights reserved)(journal abstract

Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots

Safety is paramount for mobile robotic platforms such as self-driving cars and unmanned aerial vehicles. This work is devoted to a task that is indispensable for safety yet was largely overlooked in the past -- detecting obstacles that are of very thin structures, such as wires, cables and tree branches. This is a challenging problem, as thin objects can be problematic for active sensors such as lidar and sonar and even for stereo cameras. In this work, we propose to use video sequences for thin obstacle detection. We represent obstacles with edges in the video frames, and reconstruct them in 3D using efficient edge-based visual odometry techniques. We provide both a monocular camera solution and a stereo camera solution. The former incorporates Inertial Measurement Unit (IMU) data to solve scale ambiguity, while the latter enjoys a novel, purely vision-based solution. Experiments demonstrated that the proposed methods are fast and able to detect thin obstacles robustly and accurately under various conditions.Comment: Appeared at IEEE CVPR 2017 Workshop on Embedded Visio