首页> 外文期刊>IEEE Transactions on Pattern Analysis and Machine Intelligence >Shape description with a space-variant sensor: algorithms for scan-path, fusion, and convergence over multiple scans
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Shape description with a space-variant sensor: algorithms for scan-path, fusion, and convergence over multiple scans

机译:带有空间变量传感器的形状描述:用于多次扫描的扫描路径,融合和收敛的算法

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The authors discuss three algorithms related to the blending of a single scene from multiple frames acquired from a space-variant sensor. Given a series of space-variant contour-based scenes with different fixation points, they show how to fuse these into a single multiscan view, which incorporates the information present in the individual scans. They demonstrate an (attentional) algorithm which recursively examines the current knowledge of the scene in order best to choose the next fixation point in terms of focusing attention on regions of maximum boundary curvature. They discuss a simple metric for evaluating convergence over a scan path. This may be used to compare the performance of various attentional algorithms. They discuss their work in light of both machine and biological vision.
机译:作者讨论了三种与从空间变量传感器获取的多个帧中的单个场景融合相关的算法。给定一系列具有不同注视点的基于空间变化的基于轮廓的场景,它们显示了如何将它们融合到单个多扫描视图中,该视图合并了各个扫描中存在的信息。他们展示了一种(注意力)算法,该算法递归地检查场景的当前知识,以便在将注意力集中在最大边界曲率的区域上,从而最好地选择下一个固定点。他们讨论了用于评估扫描路径上的收敛性的简单指标。这可以用来比较各种注意力算法的性能。他们从机器和生物学的角度来讨论他们的工作。

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