9,092 research outputs found

    A USB3.0 FPGA Event-based Filtering and Tracking Framework for Dynamic Vision Sensors

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    Dynamic vision sensors (DVS) are frame-free sensors with an asynchronous variable-rate output that is ideal for hard real-time dynamic vision applications under power and latency constraints. Post-processing of the digital sensor output can reduce sensor noise, extract low level features, and track objects using simple algorithms that have previously been implemented in software. In this paper we present an FPGA-based framework for event-based processing that allows uncorrelated-event noise removal and real-time tracking of multiple objects, with dynamic capabilities to adapt itself to fast or slow and large or small objects. This framework uses a new hardware platform based on a Lattice FPGA which filters the sensor output and which then transmits the results through a super-speed Cypress FX3 USB microcontroller interface to a host computer. The packets of events and timestamps are transmitted to the host computer at rates of 10 Mega events per second. Experimental results are presented that demonstrate a low latency of 10us for tracking and computing the center of mass of a detected object.Ministerio de Economía y Competitividad TEC2012-37868-C04-0

    Dynamic Decomposition of Spatiotemporal Neural Signals

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    Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals

    Event-based Vision: A Survey

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    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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