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Binary visual features for ROV motion estimation

机译:ROV运动估计的二进制视觉功能

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Binary feature descriptors are a recent and promising trend in the computer vision field. Nonetheless, they are not yet enough studied when compared to the more established floating-point features. Thus, the need of testing this kind of feature descriptors arises. In particular, in the underwater domain very few works used binary feature descriptors. Therefore, this article tries to explore this recent trend and to test the latest algorithms of this kind. The context of application is Remotely Operated Vehicle (ROV) motion estimation. Experimental data is used to validate each approach and both a qualitative and quantitative analysis is shown. The results show that BRIEF is the best approach for this kind of application.
机译:二进制特征描述符是计算机视觉领域的最新和有希望的趋势。尽管如此,与更熟悉的浮点特征相比,它们尚未足够。因此,出现了测试这种特征描述符的需要。特别是,在水下域中很少有效使用二进制特征描述符。因此,本文试图探讨这一趋势的最新趋势并测试此类最新算法。应用的上下文是远程操作的车辆(ROV)运动估计。实验数据用于验证每个方法,并显示定性和定量分析。结果表明,这简要是这种应用的最佳方法。

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