Detecting dim point target in infrared image sequences using probalilistic neural network

Haixin Chen, Zhenkang Shen, Huihuang Chen
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引用次数: 3

Abstract

In spite of many advances of IR imaging technology that have been achieved, the detection of dim point target from infrared clutter backgrounds still remains a key problem in real-time IR system. We present a new detection scheme based on a linear detector and an improved probabilistic neural network classifier for small SNR, moving point targets detection in strong infrared noise and clutter backgrounds. Computer simulation was conducted, and simulation results confirmed the validity of the detection scheme.<>
利用概率神经网络检测红外图像序列中的弱点目标
尽管红外成像技术已经取得了许多进步,但在红外杂波背景下弱小目标的检测仍然是实时红外系统中的一个关键问题。提出了一种基于线性检测器和改进的概率神经网络分类器的小信噪比运动点目标检测方案,用于强红外噪声和杂波背景下的运动点目标检测。进行了计算机仿真,仿真结果验证了该检测方案的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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