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Robust Real-Time Pattern Matching Using Bayesian Sequential Hypothesis Testing

机译:使用贝叶斯顺序假设检验的鲁棒实时模式匹配

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This paper describes a method for robust real time pattern matching. We first introduce a family of image distance measures, the "Image Hamming Distance Family". Members of this family are robust to occlusion, small geometrical transforms, light changes and non-rigid deformations. We then present a novel Bayesian framework for sequential hypothesis testing on finite populations. Based on this framework, we design an optimal rejection/acceptance sampling algorithm. This algorithm quickly determines whether two images are similar with respect to a member of the Image Hamming Distance Family. We also present a fast framework that designs a near-optimal sampling algorithm. Extensive experimental results show that the sequential sampling algorithm performance is excellent. Implemented on a Pentium 4 3GHz processor, detection of a pattern with 2197 pixels, in 640x480 pixel frames, where in each frame the pattern rotated and was highly occluded, proceeds at only 0.022 seconds per frame.
机译:本文介绍了一种鲁棒的实时模式匹配方法。我们首先介绍一系列图像距离度量,即“图像汉明距离家族”。该族的成员对遮挡,小几何变换,光线变化和非刚性变形具有鲁棒性。然后,我们提出了一个新颖的贝叶斯框架,用于对有限总体进行顺序假设检验。在此框架的基础上,我们设计了最佳拒绝/接受采样算法。该算法可以快速确定两个图像相对于图像汉明距离家族的成员是否相似。我们还提出了设计接近最佳采样算法的快速框架。大量的实验结果表明,顺序采样算法的性能非常好。在Pentium 4 3GHz处理器上实施,在640x480像素帧中检测到2197个像素的模式,其中在每个帧中旋转的模式都被高度遮挡,每帧仅进行0.022秒。

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