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Fast Exact Euclidean Distance (FEED): A New Class of Adaptable Distance Transforms

机译:快速精确欧几里德距离(FEED):一类新的自适应距离变换

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A new unique class of foldable distance transforms of digital images (DT) is introduced, baptized: Fast exact euclidean distance (FEED) transforms. FEED class algorithms calculate the DT startingdirectly from the definition or rather its inverse. The principle of FEED class algorithms is introduced, followed by strategies for their efficient implementation. It is shown that FEED class algorithms unite properties of ordered propagation, raster scanning, and independent scanning DT. Moreover, FEED class algorithms shown to have a unique property: they can be tailored to the images under investigation. Benchmarks are conducted on both the Fabbri et al. data set and on a newly developed data set. Three baseline, three approximate, and three state-of-the-art DT algorithms were included, in addition to two implementations of FEED class algorithms. It illustrates that FEED class algorithms i) provide truly exact Euclidean DT; ii) do no suffer from disconnected Voronoi tiles, which is a unique feature for non-parallel but fast DT; iii) outperform any other approximate and exact Euclidean DT with its time complexity $O(N)$ , even after their optimization; and iv) are unequaled in that they can be adapted to the characteristics of the image class at hand.
机译:引入了一种新的独特类别的可折叠数字图像(DT)转换:快速精确欧几里德距离(FEED)转换。 FEED类算法直接从定义开始计算DT,或者直接从其 inverse 开始计算。介绍了FEED类算法的原理,然后介绍了有效实施策略。结果表明,FEED类算法结合了有序传播,光栅扫描和独立扫描DT的属性。此外,FEED类算法具有独特的属性:可以针对正在研究的图像量身定制它们。对Fabbri等人进行基准测试。数据集和新开发的数据集。除了FEED类算法的两个实现之外,还包括三个基线,三个近似和三个最新的DT算法。它说明了FEED类算法:i)提供真正准确的欧几里得DT; ii)不用断开Voronoi磁贴,这是非并行但快速DT的独特功能; iii)其时间复杂度优于任何其他近似和精确的欧几里得DT,其时间复杂度 $ O(N)$ ;和iv)无与伦比,因为它们可以适应当前图像类别的特征。

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