To improve the quality of the infrared image and enhance the information of the object,a dual band infrared image fusion method based on feature extraction and a novel multiple pulse coupled neural network(multi-PCNN)...To improve the quality of the infrared image and enhance the information of the object,a dual band infrared image fusion method based on feature extraction and a novel multiple pulse coupled neural network(multi-PCNN)is proposed.In this multi-PCNN fusion scheme,the auxiliary PCNN which captures the characteristics of feature image extracting from the infrared image is used to modulate the main PCNN,whose input could be original infrared image.Meanwhile,to make the PCNN fusion effect consistent with the human vision system,Laplacian energy is adopted to obtain the value of adaptive linking strength in PCNN.After that,the original dual band infrared images are reconstructed by using a weight fusion rule with the fire mapping images generated by the main PCNNs to obtain the fused image.Compared to wavelet transforms,Laplacian pyramids and traditional multi-PCNNs,fusion images based on our method have more information,rich details and clear edges.展开更多
ProtoPNet proposed by Chen et al.is able to provide interpretability that conforms to human intuition,but it requiresmany iterations of training to learn class-specific prototypes and does not support few-shot learnin...ProtoPNet proposed by Chen et al.is able to provide interpretability that conforms to human intuition,but it requiresmany iterations of training to learn class-specific prototypes and does not support few-shot learning.We propose the few-shot learning version of ProtoPNet by using MAML,enabling it to converge quickly on different classification tasks.We test our model on the Omniglot and MiniImagenet datasets and evaluate their prototype interpretability.Our experiments showthatMAML-ProtoPNet is a transparent model that can achieve or even exceed the baseline accuracy,and its prototype can learn class-specific features,which are consistent with our human recognition.展开更多
基金Supported by the National Natural Science Foundation of China(60905012,60572058)
文摘To improve the quality of the infrared image and enhance the information of the object,a dual band infrared image fusion method based on feature extraction and a novel multiple pulse coupled neural network(multi-PCNN)is proposed.In this multi-PCNN fusion scheme,the auxiliary PCNN which captures the characteristics of feature image extracting from the infrared image is used to modulate the main PCNN,whose input could be original infrared image.Meanwhile,to make the PCNN fusion effect consistent with the human vision system,Laplacian energy is adopted to obtain the value of adaptive linking strength in PCNN.After that,the original dual band infrared images are reconstructed by using a weight fusion rule with the fire mapping images generated by the main PCNNs to obtain the fused image.Compared to wavelet transforms,Laplacian pyramids and traditional multi-PCNNs,fusion images based on our method have more information,rich details and clear edges.
文摘ProtoPNet proposed by Chen et al.is able to provide interpretability that conforms to human intuition,but it requiresmany iterations of training to learn class-specific prototypes and does not support few-shot learning.We propose the few-shot learning version of ProtoPNet by using MAML,enabling it to converge quickly on different classification tasks.We test our model on the Omniglot and MiniImagenet datasets and evaluate their prototype interpretability.Our experiments showthatMAML-ProtoPNet is a transparent model that can achieve or even exceed the baseline accuracy,and its prototype can learn class-specific features,which are consistent with our human recognition.
