Hardware architecture for hierarchical segmentation in foveal images
Hardware architecture for hierarchical segmentation in foveal images
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DOI：
10.1002/ima.20019
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发表时间：
2004-01-01
影响因子：
3.3
通讯作者：
Sandoval, F
中科院分区：
文献类型：
--
作者：
Coslado, FJ;Camacho, P;Sandoval, F
中央凹传感器可以显着提高主动视觉系统的性能，因为它们能够处理宽视场，同时通过空间变化传感减少数据/带宽。为了处理多分辨率图像和相关数据结构，应用了新的分层处理来最小化数据通信和检索。在本文中，我们提出了一个硬件平台，该平台在基于笛卡尔点阵拓扑的这些分层结构之一中实现了级别顺序分割算法。该平台以 25 至 85 帧/秒的速度实时运行，使用数字统一分辨率相机作为源来生成和处理多分辨率图像。 (C) 2004 年 Wiley 期刊公司。
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. (C) 2004 Wiley Periodicals, Inc.