Dynamic Vision Sensor (DVS) is a new type of neuromorphic event-based sensor, which has an innate advantage in capturing fast-moving objects. Due to the interference of DVS hardware itself and many external factors, n...
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Dynamic Vision Sensor (DVS) is a new type of neuromorphic event-based sensor, which has an innate advantage in capturing fast-moving objects. Due to the interference of DVS hardware itself and many external factors, noise is unavoidable in the output of DVS. Different from frame/image with structural data, the output of DVS is in the form of address-event representation (AER), which means that the traditional denoising methods cannot be used for the output (i.e., event stream) of the DVS. In this paper, we propose a novel event stream denoising method based on probabilistic undirected graph model (PUGM). The motion of objects always shows a certain regularity/trajectory in space and time, which reflects the spatio-temporal correlation between effective events in the stream. Meanwhile, the event stream of DVS is composed by the effective events and random noise. Thus, a probabilistic undirected graph model is constructed to describe such priori knowledge (i.e., spatio-temporal correlation). The undirectedgraphmodel is factorized into the product of the cliques energy function, and the energy function is defined to obtain the complete expression of the joint probability distribution. Better denoising effect means a higher probability (lower energy), which means the denoising problem can be transfered into energy optimization problem. Thus, the iterated conditional modes (ICM) algorithm is used to optimize the model to remove the noise. Experimental results on denoising show that the proposed algorithm can effectively remove noise events. Moreover, with the preprocessing of the proposed algorithm, the recognition accuracy on AER data can be remarkably promoted. The source code of the proposed method is available at https://***/wjj/***.
As a novel asynchronous-driven cameras, event-based sensors are with high sensitivity, fast speed, low power consumption and low data volume, but with abundant noise. Since the output of event-based sensors is in the ...
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ISBN:
(数字)9781509066315
ISBN:
(纸本)9781509066315
As a novel asynchronous-driven cameras, event-based sensors are with high sensitivity, fast speed, low power consumption and low data volume, but with abundant noise. Since the output of event-based sensors is in the form of address-event-representation (AER), the traditional frame-based denoising method cannot be used. In this paper, we introduce a novel event stream denoising method for such sensors. Effective events tend to show temporal and spatial regularity, while noise events show a kind of randomness. Thus, we build a probabilistic undirected graph model to describe this difference, with which the denoising problem is converted to a probability maximization problem. Then, the model is decomposed into the product of the energy function on the maximum cliques, and the iterated condition model (ICM) is used for energy minimization to obtain the denoised event stream. Experiments show that our method can effectively remove noise events directly from the event stream and significantly improve event recognition rate.
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