Sylvestcarstensen8464

The sensitivity and efficiency of the SPN indicator is examined and demonstrated. Then, a speckle-free SAR ship detection approach is established based on the SPN indicator. The detection flowchart is also given. buy Lenvatinib Experimental and comparison studies are carried out with three kinds of spaceborne SAR datasets in terms of different polarizations. The proposed method achieves the best SAR ship detection performances with the highest figures of merits (FoM) of 97.14%, 90.32% and 93.75% for the used Radarsat-2, GaoFen-3 and Sentinel-1 datasets, accordingly.Recent research has witnessed advances in facial image editing tasks including face swapping and face reenactment. However, these methods are confined to dealing with one specific task at a time. In addition, for video facial editing, previous methods either simply apply transformations frame by frame or utilize multiple frames in a concatenated or iterative fashion, which leads to noticeable visual flickers. In this paper, we propose a unified temporally consistent facial video editing framework termed UniFaceGAN. Based on a 3D reconstruction model and a simple yet efficient dynamic training sample selection mechanism, our framework is designed to handle face swapping and face reenactment simultaneously. To enforce the temporal consistency, a novel 3D temporal loss constraint is introduced based on the barycentric coordinate interpolation. Besides, we propose a region-aware conditional normalization layer to replace the traditional AdaIN or SPADE to synthesize more context-harmonious results. Compared with the state-of-the-art facial image editing methods, our framework generates video portraits that are more photo-realistic and temporally smooth.Weakly supervised temporal action localization is a challenging task as only the video-level annotation is available during the training process. To address this problem, we propose a two-stage approach to generate high-quality frame-level pseudo labels by fully exploiting multi-resolution information in the temporal domain and complementary information between the appearance (i.e., RGB) and motion (i.e., optical flow) streams. In the first stage, we propose an Initial Label Generation (ILG) module to generate reliable initial frame-level pseudo labels. Specifically, in this newly proposed module, we exploit temporal multi-resolution consistency and cross-stream consistency to generate high quality class activation sequences (CASs), which consist of a number of sequences with each sequence measuring how likely each video frame belongs to one specific action class. In the second stage, we propose a Progressive Temporal Label Refinement (PTLR) framework to iteratively refine the pseudo labels, in which we use a set of selected frames with highly confident pseudo labels to progressively train two networks and better predict action class scores at each frame. Specifically, in our newly proposed PTLR framework, two networks called Network-OTS and Network-RTS, which are respectively used to generate CASs for the original temporal scale and the reduced temporal scales, are used as two streams (i.e., the OTS stream and the RTS stream) to refine the pseudo labels in turn. By this way, multi-resolution information in the temporal domain is exchanged at the pseudo label level, and our work can help improve each network/stream by exploiting the refined pseudo labels from another network/stream. Comprehensive experiments on two benchmark datasets THUMOS14 and ActivityNet v1.3 demonstrate the effectiveness of our newly proposed method for weakly supervised temporal action localization.Cavitation is the fundamental physical mechanism of various focused ultrasound (FUS)-mediated therapies in the brain. Accurately knowing the 3D location of cavitation in real-time can improve the targeting accuracy and avoid off-target tissue damage. Existing techniques for 3D passive transcranial cavitation detection require the use of expensive and complicated hemispherical phased arrays with 128 or 256 elements. The objective of this study was to investigate the feasibility of using four sensors for transcranial 3D localization of cavitation. Differential microbubble cavitation detection combined with the time difference of arrival algorithm was developed for the localization using the four sensors. Numerical simulation using k-Wave toolbox was performed to validate the proposed method for transcranial cavitation source localization. The sensors with a center frequency of 2.25 MHz and a 6-dB bandwidth of 1.39 MHz were used to locate cavitation generated by FUS (500 kHz) sonication of microbubbles that were injected into a tube positioned inside an ex vivo human skullcap. Cavitation emissions from the microbubbles were detected transcranially using the four sensors. Both simulation and experimental studies found that the proposed method achieved accurate 3D cavitation localization. The accuracy of the localization method with the skull was measured to be 1.9 ± 1.0 mm when the cavitation source was located within 30 mm from the geometric center of the sensor network, which was not significantly different from that without the skull (1.7 ± 0.5 mm). The accuracy decreased as the cavitation source was away from the geometric center of the sensor network. It also decreased as the pulse length increased. Its accuracy was not significantly affected by the sensor position relative to the skull. In summary, four sensors combined with the proposed localization algorithm offer a simple approach for 3D transcranial cavitation localization.In this work, we propose a novel Convolutional Neural Network (CNN) architecture for the joint detection and matching of feature points in images acquired by different sensors using a single forward pass. The resulting feature detector is tightly coupled with the feature descriptor, in contrast to classical approaches (SIFT, etc.), where the detection phase precedes and differs from computing the descriptor. Our approach utilizes two CNN subnetworks, the first being a Siamese CNN and the second, consisting of dual non-weight-sharing CNNs. This allows simultaneous processing and fusion of the joint and disjoint cues in the multimodal image patches. The proposed approach is experimentally shown to outperform contemporary state-of-the-art schemes when applied to multiple datasets of multimodal images. It is also shown to provide repeatable feature points detections across multi-sensor images, outperforming state-of-the-art detectors. To the best of our knowledge, it is the first unified approach for the detection and matching of such images.