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Building smarter sensors - lessons learned from computer vision

机译:构建更智能的传感器-从计算机视觉中学到的经验教训

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This paper describes recent work in the field of computer vision and relates the results to the much broader class of smart sensors. The sensor requirements for an autonomous mobile robot capable of map building and path planning are described. We show that when the sensor input noise is taken into consideration, a conventional CCD array is unable to provide a robust representation of an object, such that the object can be recognised regardless of the scale of the image plane. In contrast to this, biological based retinal arrays are able to achieve this (within signal limits). The paper concludes with the perspective that all sensor systems are data dependent. This is of little concern if the sensor consists of a single element, but becomes more important as larger arrays (with broader selectivity) are fabricated. These sensors may have to emulate biological systems, in an analogous manner to a retinal camera.
机译:本文介绍了计算机视觉领域的最新工作,并将结果与​​更广泛的智能传感器类别联系起来。描述了能够进行地图构建和路径规划的自主移动机器人的传感器要求。我们显示出,当考虑到传感器输入噪声时,常规的CCD阵列无法提供对象的鲁棒表示,因此无论图像平面的大小如何,都可以识别该对象。与此相反,基于生物的视网膜阵列能够做到这一点(在信号限制内)。本文以所有传感器系统都依赖于数据的观点作为结论。如果传感器由单个元素组成,这几乎没有问题,但是随着制造更大的阵列(具有更宽的选择性)而变得尤为重要。这些传感器可能必须以类似于视网膜相机的方式模拟生物系统。

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