Physiologically motivated image fusion for object detection using a pulse coupled neural network
Physiologically motivated image fusion for object detection using a pulse coupled neural network
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DOI：
10.1109/72.761712
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发表时间：
1999-05
影响因子：
--
通讯作者：
R. Broussard;S. Rogers;M. Oxley;G. L. Tarr
中科院分区：
文献类型：
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作者：
R. Broussard;S. Rogers;M. Oxley;G. L. Tarr
本文提出了第一个生理激励脉冲耦合神经网络（PCNN）的图像融合网络的目标检测。灵长类动物的视觉处理原理，如期望驱动的过滤，状态相关的调制，时间同步，和多个处理路径被应用到创建一个生理动机的图像融合网络。PCNN用于融合几种目标检测技术的结果，以提高目标检测精度。图像处理技术（小波、形态学等）用于提取目标特征，PCNN用于通过分割和融合信息来集中注意力。所得到的图像融合网络的对象检测性能表现在乳房X线照片和前视红外雷达（FLIR）图像。该网络在FLIR图像中消除了94%的错误检测，而没有消除任何真实检测，并在乳房X线照片中消除了46%的错误检测，而仅消除了7%的真实检测。该模型超过了通过任何单独的过滤方法或通过逻辑与单个对象检测技术结果获得的准确性。
This paper presents the first physiologically motivated pulse coupled neural network (PCNN)-based image fusion network for object detection. Primate vision processing principles, such as expectation driven filtering, state dependent modulation, temporal synchronization, and multiple processing paths are applied to create a physiologically motivated image fusion network. PCNN's are used to fuse the results of several object detection techniques to improve object detection accuracy. Image processing techniques (wavelets, morphological, etc.) are used to extract target features and PCNN's are used to focus attention by segmenting and fusing the information. The object detection property of the resulting image fusion network is demonstrated on mammograms and Forward Looking Infrared Radar (FLIR) images. The network removed 94% of the false detections without removing any true detections in the FLIR images and removed 46% of the false detections while removing only 7% of the true detections in the mammograms. The model exceeded the accuracy obtained by any individual filtering methods or by logical ANDing the individual object detection technique results.