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首页> 外文期刊>The international journal of virtual reality >Natural Feature Detection on Mobile Phones with 3D FAST
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Natural Feature Detection on Mobile Phones with 3D FAST

机译：具有3D FAST的手机自然特征检测

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摘要

In this paper, we present a novel feature detection approach designed for mobile devices, showing optimized solutions for both detection and description. It is based on FAST (Features from Accelerated Segment Test) and named 3D FAST. Being robust, scale-invariant and easy to compute, it is a candidate for augmented reality (AR) applications running on low performance platforms. Using simple calculations and machine learning, FAST is a feature detection algorithm known to be efficient but not very robust in addition to its lack of scale information. Our approach relies on gradient images calculated for different scale levels on which a modified9 FAST algorithm operates to obtain the values of the corner response function. We combine the detection with an adapted version of SURF (Speed Up Robust Features) descriptors, providing a system with all means to implement feature matching and object detection. Experimental evaluation on a Symbian OS device using a standard image set and comparison with SURF using Hessian matrix-based detector is included in this paper, showing improvements in speed (compared to SURF) and robustness (compared to FAST).
机译：在本文中，我们提出了一种专为移动设备设计的新颖特征检测方法，展示了用于检测和描述的优化解决方案。它基于FAST（加速段测试的功能）并命名为3D FAST。它坚固，可缩放且易于计算，是运行在低性能平台上的增强现实（AR）应用程序的候选对象。使用简单的计算和机器学习，FAST是一种功能检测算法，众所周知，它虽然缺乏规模信息，但效率很高，但不够鲁棒。我们的方法依赖于针对不同比例级别计算的梯度图像，经过修改的FAST算法在该图像上进行操作以获取角响应函数的值。我们将检测与SURF（加速鲁棒特征）描述符的适应版本结合在一起，为系统提供了实现特征匹配和对象检测的所有方法。本文包括使用标准图像集对Symbian OS设备进行的实验评估，以及使用基于Hessian矩阵的检测器与SURF进行比较，从而显示出速度（与SURF相比）和鲁棒性（与FAST相比）得到了改善。

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