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Detection of point targets in image sequences by hypothesis testing: a temporal test first approach

机译:通过假设检验检测图像序列中的点目标:时间检验优先方法

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This paper addresses the problem of designing an efficient and effective image sequence processing scheme that will successfully detect very small (point) targets in a cluttered background when both the target and clutter are moving through the image scene. The specific application area was detection of targets such as airplanes in infrared (IR) image sequences of a cloudy sky which have been taken by a stationary camera. In general we assume that targets are typically one to two pixels in extent and move only a fraction of a pixel per frame, are often low amplitude, and are found in scenes which also contain evolving clutter, e.g. clouds. Our algorithm is based on signal processing and detection theory, includes a perfect measurement performance analysis, and can be made computationally efficient compared to other approaches. Thus the algorithm could be applicable to other image sequence processing scenarios, using other acquisition systems besides IR, such as detection of small moving objects or structures in a biomedical or biological imaging scenario or the detection of satellites, meteors or other celestial bodies in night sky imagery acquired using a telescope. We present a GLRT solution, perfect measurement analysis including ROC curves, and results using real-world infrared data.
机译:本文解决了设计一种高效且有效的图像序列处理方案的问题,该方案将在杂物背景和杂物同时移动通过图像场景时成功检测杂物背景中的非常小的(点)目标。具体应用领域是检测目标,例如飞机在多云的天空的红外(IR)图像序列中的位置,这些图像已由固定照相机拍摄。通常,我们假设目标的范围通常为一到两个像素,每帧仅移动像素的一小部分,幅度通常较低,并且在还包含不断变化的杂波的场景(例如场景)中被发现。云。我们的算法基于信号处理和检测理论,包括完美的测量性能分析,并且与其他方法相比,可以使计算效率更高。因此,该算法可以适用于其他图像序列处理场景,使用除IR之外的其他采集系统,例如在生物医学或生物成像场景中检测小型移动物体或结构,或在夜空中检测卫星,流星或其他天体使用望远镜获取的图像。我们提供了GLRT解决方案,完善的测量分析(包括ROC曲线)和使用真实红外数据的结果。

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