视频的时空自适应三维卡尔曼滤波器。
Spatio-temporal adaptive 3-D Kalman filter for video.
机构信息
Samsung Semicond., San Jose, CA.
出版信息
IEEE Trans Image Process. 1997;6(3):414-24. doi: 10.1109/83.557351.
This paper presents three-dimensional (spatio-temporal) Kalman filters for video as the extension of the two-dimensional (2-D) reduced update Kalman filter (RUKF) approach for images. We start out with three-dimensional (3-D) RUKF, a shift-invariant recursive estimator with efficiency advantages over the 3-D Wiener filter. Then, we turn to the motion-compensated extension MC-RUKF, which gives improved performance when coupled with a motion estimator. Since motion compensation sometimes fails, causing severe fluctuations in temporal correlation, we then present multimodel MC-RUKF, to adapt to variation in temporal and spatial correlation, by detecting the local image model out of a class, and using it in MC-RUKF. Finally, we introduce a novel multiscale model detection algorithm for use in high noise environments.
本文提出了视频的三维（时空）卡尔曼滤波器，作为二维（2-D）简化更新卡尔曼滤波器（RUKF）方法在图像上的扩展。我们从三维（3-D）RUKF 开始，这是一种平移不变递归估计器，与 3-D Wiener 滤波器相比具有效率优势。然后，我们转向运动补偿扩展 MC-RUKF，当与运动估计器结合使用时，它可以提高性能。由于运动补偿有时会失败，导致时间相关的严重波动，因此我们提出了多模型 MC-RUKF，通过从一类中检测局部图像模型，并在 MC-RUKF 中使用它，来适应时间和空间相关的变化。最后，我们引入了一种新的多尺度模型检测算法，用于高噪声环境。
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