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Depth reconstruction uncertainty analysis and improvement - The dithering approach

机译:深度重建不确定性分析和改进-抖动方法

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The depth spatial quantization uncertainty is one of the factors which influence the depth reconstruction accuracy caused by a discrete sensor. This paper discusses the quantization uncertainty distribution, introduces a mathematical model of the uncertainty interval range, and analyzes the movements of the sensors in an Intelligent Vision Agent System. Such a system makes use of multiple sensors which control the deployment and autonomous servo of the system. This paper proposes a dithering algorithm which reduces the depth reconstruction uncertainty. The algorithm assures high accuracy from a few images taken by low-resolution sensors. The dither signal is estimated and then generated through an analysis of the iso-disparity planes. The signal allows for control of the camera movement. The proposed approach is validated and compared with a direct triangulation method. The simulation results are reported in terms of depth reconstruction error statistics. The physical experiment shows that the dithering method reduces the depth reconstruction error.
机译:深度空间量化不确定性是影响由离散传感器引起的深度重建精度的因素之一。本文讨论了量化不确定性分布,介绍了不确定性区间范围的数学模型,并分析了智能视觉代理系统中传感器的运动。这样的系统利用多个传感器来控制系统的部署和自主伺服。本文提出了一种减少深度重建不确定性的抖动算法。该算法可确保由低分辨率传感器拍摄的一些图像具有很高的准确性。估计抖动信号,然后通过分析等差平面来生成。该信号允许控制摄像机的运动。所提出的方法得到了验证,并与直接三角剖分方法进行了比较。根据深度重建误差统计报告了模拟结果。物理实验表明,抖动方法减小了深度重建误差。

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