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Bio-inspired Motion Estimation with Event-Driven Sensors

机译:事件驱动传感器的生物启发运动估计

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This paper presents a method for image motion estimation for event-based sensors. Accurate and fast image flow estimation still challenges Computer Vision. A new paradigm based on asynchronous event-based data provides an interesting alternative and has shown to provide good estimation at high contrast contours by estimating motion based on very accurate timing. However, these techniques still fail in regions of high-frequency texture. This work presents a simple method for locating those regions, and a novel phase-based method for event sensors that estimates more accurately these regions. Finally, we evaluate and compare our results with other state-of-the-art techniques.
机译:本文提出了一种基于事件的传感器的图像运动估计方法。准确,快速的图像流估计仍对计算机视觉构成挑战。基于异步基于事件的数据的新范例提供了一种有趣的替代方法,并已显示可通过基于非常精确的时序估算运动来在高对比度轮廓上提供良好的估算。但是,这些技术在高频纹理区域仍然失败。这项工作提出了一种用于定位这些区域的简单方法,以及一种用于事件传感器的基于阶段的新颖方法,该方法可以更准确地估计这些区域。最后,我们评估结果并将其与其他最新技术进行比较。

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