Diederik Paul Moeys

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This paper describes the application of a Convolutional Neural Network (CNN) in the context of a predator/prey scenario. The CNN is trained and run on data from a Dynamic and Active Pixel Sensor (DAVIS) mounted on a Summit XL robot (the predator), which follows another one (the prey). The CNN is driven by both conventional image frames and dynamic vision(More)
This paper reports an object tracking algorithm for a moving platform using the dynamic and active-pixel vision sensor (DAVIS). It takes advantage of both the active pixel sensor (APS) frame and dynamic vision sensor (DVS) event outputs from the DAVIS. The tracking is performed in a three step-manner: regions of interest (ROIs) are generated by a(More)
This paper describes the software and FPGA implementation of a Retinal Ganglion Cell model which detects moving objects. It is shown how this processing, in conjunction with a Dynamic Vision Sensor as its input, can be used to extrapolate information about object position. Software-wise, a system based on an array of these of RGCs has been developed in(More)
This paper describes a VLSI automatic quality control pitch detector circuit which can be used for detecting the identity of a unique bird. The detector is based on a previous VLSI model of the local gain control mechanism of the outer hair cells of the biological cochlea. This work presents characterization results from a 20-channel chip fabricated in a(More)
This paper introduces the first simulations and measurements of event data obtained from the first Dynamic and Active Vision Sensors (DAVIS) with RGBW color filters. The absolute quantum efficiency spectral responses of the RGBW photodiodes were measured, the behavior of the color-sensitive DVS pixels were simulated and measured, and reconstruction through(More)
This demonstration shows how object detection and tracking are possible thanks to a new implementation which takes inspiration from the visual processing of a particular type of ganglion cell in the retina. This demonstration is associated with the track 10.4 Neuromorphic Sensing And Processing. Associated paper submission identifier: 1413. I. DEMONSTRATION(More)
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