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A Registration Method Based on Nature Feature with KLT Tracking Algorithm for Wearable Computers

机译:基于自然特征与KLT跟踪算法的可穿戴计算机注册方法

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KLT algorithm has been widely used in the registration process for natural features tracking in augmented reality (AR) systems. However, KLT is vulnerable by surrounding environments, and the feature points on screen borderlines may not be tracked persistently. Homographic matrices cannot be calculated accurately due to these disadvantages, which will result in registration failure. In this paper, KLT algorithm was improved based on the updating strategy of feature-points set, moreover, we applied RANSAC algorithm to filter mismatched feature points for calculating homographic matrices precisely. The new method was implemented in the prototype system which ran on a wearable computer, and the experiments results show that our system can not only track the feature points stably but also ensure the accuracy and the effectiveness of registration.
机译:KLT算法已在配准过程中广泛用于增强现实(AR)系统中的自然特征跟踪。但是,KLT容易受到周围环境的影响,并且屏幕边界线上的特征点可能无法持久跟踪。由于这些缺点,无法正确计算单应性矩阵,这将导致配准失败。本文在特征点集更新策略的基础上对KLT算法进行了改进,并应用RANSAC算法对不匹配的特征点进行滤波,以求出单应性矩阵的精确度。该新方法在可穿戴计算机上运行的原型系统中实现,实验结果表明我们的系统不仅可以稳定地跟踪特征点,而且可以确保配准的准确性和有效性。

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