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A Comparative Study of Matching Algorithms for Natural Markers

机译:自然标记匹配算法的比较研究

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Natural markers are increasingly being adopted by the Augmented Reality (AR) community. These systems require several steps of data processing to be carried out with the aim of allowing a less parameterized image be recognized by the computer system. Among the possibilities for this task there are the proposals that employ interest points (or feature points). The steps needed to process the natural markers using interest points include the matching (usually between one image representation and the video stream). Even having these options well known in the community, try to minimize the processing time or improve the results in terms of accuracy of matching is especially important to the development of AR applications. In this paper, we discuss the results of three comparative studies that demonstrate the advantages and disadvantages of the algorithms FLANN and Brute Force when employed in the matching step of natural markers using interest points. As one of the results, we show that in some cases, the algorithm with most computational complexity can be the better choice.
机译:增强现实(AR)社区越来越多地采用自然标记。这些系统需要执行几个步骤的数据处理,以使计算机系统可以识别较少参数化的图像。在这项任务的可能性中,有采用兴趣点(或特征点)的提议。使用兴趣点处理自然标记所需的步骤包括匹配(通常在一个图像表示和视频流之间)。即使这些选项在社区中广为人知,但在匹配精度方面,尽量减少处理时间或改善结果对于AR应用程序的开发尤为重要。在本文中,我们讨论了三项比较研究的结果,这些研究证明了FLANN和蛮力算法在使用兴趣点进行自然​​标记的匹配步骤中使用时的优缺点。作为结果之一,我们表明在某些情况下,具有最高计算复杂度的算法可能是更好的选择。

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