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Hardware Architecture for Hierarchical Segmentation in Foveal Images

机译:小凹图像中分层分割的硬件架构

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Foveal sensors can substantially increase the performance of active vision systems because of their ability to handle wide field of view and simultaneously reduce the data/bandwidth with space variant sensing. To process the multiresolution images and associated data structures, a new hierarchical processing has been applied to minimize data communications and retrieval. In this article, we present a hardware platform that implements a level sequential segmentation algorithm in one of these hierarchical structures based on a Cartesian lattice topology. The platform operates in real time, at speeds in the range of 25 to 85 frames/s, using a digital uniform-resolution camera as the source to generate and process the multiresolution images.
机译:中央凹传感器能够处理宽视野并同时通过空间变量传感来减少数据/带宽,因此可以大大提高主动视觉系统的性能。为了处理多分辨率图像和关联的数据结构,已应用新的分层处理以最小化数据通信和检索。在本文中,我们介绍了一个硬件平台,该平台在基于笛卡尔网格拓扑的这些层次结构之一中实现了级别顺序分段算法。该平台使用数字均匀分辨率相机作为源来生成和处理多分辨率图像,以25至85帧/秒的速度实时运行。

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