Hartman Hancock (kittyfork5)

To show the effectiveness of the workflow, we conducted multiple case studies with domain experts from different application areas and with different data modalities.The convolutional neural network (CNN)-based multi-focus image fusion methods which learn the focus map from the source images have greatly enhanced fusion performance compared with the traditional methods. However, these methods have not yet reached a satisfactory fusion result, since the convolution operation pays too much attention on the local region and generating the focus map as a local classification (classify each pixel into focus or de-focus classes) problem. In this article, a global-feature encoding U-Net (GEU-Net) is proposed for multi-focus image fusion. In the proposed GEU-Net, the U-Net network is employed for treating the generation of focus map as a global two-class segmentation task, which segments the focused and defocused regions from a global view. For improving the global feature encoding capabilities of U-Net, the global feature pyramid extraction module (GFPE) and global attention connection upsample module (GACU) are introduced to effectively extract and utilize the global semantic and edge information. The perceptual loss is added to the loss function, and a large-scale dataset is constructed for boosting the performance of GEU-Net. Experimental results show that the proposed GEU-Net can achieve superior fusion performance than some state-of-the-art methods in both human visual quality, objective assessment and network complexity.Traditional tensor decomposition methods, e.g., two dimensional principal component analysis and two dimensional singular value decomposition, that minimize mean square errors, are sensitive to outliers. To overcome this problem, in this paper we propose a new robust tensor decomposition method using generalized correntropy criterion (Corr-Tensor). A Lagrange multiplier method is used to effectively optimize the generalized correntropy objective function in an iterative manner. The Corr-Tensor can effectively improve the robustness of tensor decomposition with the existence of outliers without introducing any extra computational cost. Experimental results demonstrated that the proposed method significantly reduces the reconstruction error on face reconstruction and improves the accuracies on handwritten digit recognition and facial image clustering.Domain adaptation aims to alleviate the distribution discrepancy between source and target domains. Most conventional methods focus on one target domain setting adapted from one or multiple source domains while neglecting the multi-target domain setting. We argue that different target domains also have complementary information, which is very important for performance improvement. In this paper, we propose an Attention-guided Multiple source-and-target Domain Adaptation (AMDA) method to capture the context dependency information on transferable regions among multiple source and target domains. The innovation points of this paper are as follows (1) We use numerous adversarial strategies to harvest sufficient information from multiple source and target domains, which extends the generalization and robustness of the feature pools. (2) We propose an intra-domain and inter-domain attention module to explore transferable context information. The proposed attention module can learn domain-invariant representations and reduce the negative transfer by focusing on transferable knowledge. Extensive experiments validate the effectiveness of our method with achieving state-of-the-art performance on several unsupervised domain adaptation datasets.The ability to monitor cavitation activity during ultrasound and microbubble-mediated procedures is of high clinical value. However, there has been little reported literature comparing the cavitation characteristics of different clinical microbubbles, nor have current clinical scanners been used to perform passive cavitation detection in