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Iterative filtering of SIFT keypoint matches for multi-view registration in Distributed Video Coding

机译:分布式视频编码中用于多视图注册的SIFT关键点匹配的迭代过滤

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摘要

Multi-view registration is an essential step in order to generate the side information for multi-view Distributed Video Coding. As stated in our previous work (Ciobanu and C6rte-Real, Multimed Tools Appl 48(3):411-436, 2010) it can be achieved by SIFT (scale-invariant feature transform) generated keypoint matches. The registration accuracy is vital for the adequate generation of side information and it directly depends on the reliable match of possibly all the available point to point correlations between two complete-overlapped views. We propose a solution to this problem based on iterative filtering of SIFT-generated keypoint matches, using the Hough transform and block matching. It aims the generic, real-life and constraint-free scenarios having an arbitrarily close angle between the two views. Practical results show an overall significant reduction of the outliers while maintaining a high rate of correct matches.
机译:为了产生用于多视图分布式视频编码的辅助信息,多视图注册是必不可少的步骤。如我们先前的工作所述(Ciobanu和C6rte-Real,Multimed Tools Appl 48(3):411-436,2010),可以通过SIFT(尺度不变特征变换)生成的关键点匹配来实现。配准精度对于充分生成辅助信息至关重要,它直接取决于两个完全重叠的视图之间可能所有可用的点对点关联的可靠匹配。我们建议使用霍夫变换和块匹配,基于对SIFT生成的关键点匹配进行迭代过滤的方法来解决此问题。它的目标是在两个视图之间具有任意接近的角度的通用,现实生活和无约束的场景。实际结果表明,异常值总体上显着降低,同时保持了较高的正确匹配率。

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