中间特征检测器:来自图像边界的稳定区域

Yannis Avrithis, Konstantinos Rapantzikos
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引用次数: 25

摘要

我们提出了一种局部特征检测器,它能够检测任意尺度和形状的区域,而不需要构建尺度空间。我们使用我们的精确线性时间算法计算图像梯度上的加权距离图,这是欧几里得空间的一种变体。我们通过扩展残基找到加权的内侧轴,通常用于Voronoi骨架。我们将中间轴分解成一个以峰和鞍点表示图像结构的图。对偶属性允许使用相同的行进方法重建区域。我们贪婪地对区域进行分组,同时考虑到对比度和形状。在这个过程中,我们根据我们的形状碎片因子来选择区域,偏爱那些被边界包围得很好的区域——甚至是不完整的区域。我们在匹配和检索实验中实现了最先进的性能,减少了内存和计算需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The medial feature detector: Stable regions from image boundaries
We present a local feature detector that is able to detect regions of arbitrary scale and shape, without scale space construction. We compute a weighted distance map on image gradient, using our exact linear-time algorithm, a variant of group marching for Euclidean space. We find the weighted medial axis by extending residues, typically used in Voronoi skeletons. We decompose the medial axis into a graph representing image structure in terms of peaks and saddle points. A duality property enables reconstruction of regions using the same marching method. We greedily group regions taking both contrast and shape into account. On the way, we select regions according to our shape fragmentation factor, favoring those well enclosed by boundaries—even incomplete. We achieve state of the art performance in matching and retrieval experiments with reduced memory and computational requirements.
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