Broussard R P, Rogers S K, Oxley M E, Tarr G L
Air Force Research Laboratory, Sensors Directorate, Wright-Patterson AFB, OH 45433-7303, USA.
IEEE Trans Neural Netw. 1999;10(3):554-63. doi: 10.1109/72.761712.
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.
本文提出了首个基于具有生理动机的脉冲耦合神经网络（PCNN）的用于目标检测的图像融合网络。灵长类动物视觉处理原理，如期望驱动滤波、状态依赖调制、时间同步和多条处理路径，被应用于创建一个具有生理动机的图像融合网络。PCNN被用于融合多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）被用于提取目标特征，而PCNN则通过分割和融合信息来聚焦注意力。在乳腺X光图像和前视红外雷达（FLIR）图像上展示了所得图像融合网络的目标检测性能。该网络在FLIR图像中去除了94%的误检，同时没有去除任何真实检测；在乳腺X光图像中去除了46%的误检，而仅去除了7%的真实检测。该模型的准确率超过了任何单一滤波方法或通过对各个目标检测技术结果进行逻辑与运算所获得的准确率
