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首页> 外文会议>Conference on Signal and Data Processing of Small Targets 2002, Apr 2-4, 2002, Orlando, USA >Adaptive Mean and Variance Filter for Detection of Dim Point-like Targets
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Adaptive Mean and Variance Filter for Detection of Dim Point-like Targets

机译：自适应均值和方差滤波器，用于检测昏暗点状目标

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摘要

The problem of detecting small target in IR imagery has attracted much research effort over the past few decades. As opposed to early detection algorithms which detect targets spatially in each image and then apply tracking algorithm, more recent approaches have used multiple frames to incorporate temporal as well as spatial information. They often referred to as "track before detect" algorithms. This approach has shown promising results particularly for detection of dim point-like targets. However, the computationally complexity has prohibited practical usage for such algorithms. This paper presents an adaptive, recursive and computation efficient detection method. This detection algorithm updates parameters and detects occurrence of targets as new frame arrived without storing previous frames, thus achieved recursiveness. Besides, the target temporal intensity change is modeled by two Gaussian distribution with different mean and variance. The derivation of this generalized model has taken account of the wide variation of target speed, therefore detects wider range of targets.
机译：在过去的几十年中，在红外图像中检测小目标的问题吸引了许多研究工作。与早期检测算法在空间上在每个图像中检测目标然后应用跟踪算法相反，最近的方法已经使用多个帧来合并时间和空间信息。它们通常称为“检测前跟踪”算法。这种方法已显示出令人鼓舞的结果，特别是对于检测暗点状目标。但是，计算复杂性已经禁止了这种算法的实际使用。本文提出了一种自适应，递归和计算有效的检测方法。该检测算法在不存储先前帧的情况下更新参数并在新帧到达时检测到目标的出现，从而实现了递归。此外，目标时间强度变化是通过均值和方差不同的两个高斯分布来建模的。该广义模型的推导考虑了目标速度的广泛变化，因此可以检测到较宽的目标范围。

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