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首页> 外文会议>SIBGRAPI Conference on Graphics, Patterns and Images >FOMP: A Novel Preprocessing Technique to Speed-Up the Outlier Removal from Matched Points
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FOMP: A Novel Preprocessing Technique to Speed-Up the Outlier Removal from Matched Points

机译：FOMP：一种新颖的预处理技术，可加快从匹配点中去除异常值的速度

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摘要

Image matching plays a major role in many applications, including pattern recognition and biomedical imaging. It encompasses three steps: 1) interest point selection, 2) feature extraction from each interest point, 3) features point matching. For steps 1 and 2, traditional interest point detectors/extractors have worked well. However, for step 3 even a few points incorrectly matched (outliers), might lead to an undesirable result. State-of-the-art consensus algorithms present a high time cost as the number of outlier increases. Aimed at overcoming this problem, we present FOMP, a novel preprocessing approach, that reduces the amount of outliers in the initial set of matched points by filtering out the vertices that present a higher difference among their edges in a complete graph representation of the points. The precision of traditional methods is kept, while the time is speed up in 50%. The approach removes, in average, more than 65% of outliers, while keeping over 98% of the inliers.
机译：图像匹配在许多应用中起着重要作用，包括模式识别和生物医学成像。它包括三个步骤：1）兴趣点选择，2）从每个兴趣点提取特征，3）特征点匹配。对于第1步和第2步，传统的兴趣点检测器/提取器运行良好。但是，对于第3步，即使是几个不正确匹配的点（异常值）也可能导致不良结果。随着离群值数量的增加，最新的共识算法带来了高昂的时间成本。为了克服这个问题，我们提出了一种新颖的预处理方法FOMP，该方法通过过滤出点的边缘在完整的图形表示中呈现出较高差异的顶点，从而减少了匹配点的初始集合中的异常值。保留了传统方法的精度，而时间却缩短了50％。该方法平均去除了超过65％的异常值，同时保留了超过98％的异常值。

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