Stileshartvig4558

More importantly, we propose the efficient non-greedy optimization algorithms to solve our objective and the more general L21-norm maximization problem with theoretically guaranteed convergence. Experimental results on real world data sets show the effectiveness of the proposed method for principal component analysis.Non-destructive evaluation (NDE) is a set of techniques used for material inspection and defect detection without causing damage to the inspected component. One of the commonly used non-destructive techniques is called ultrasonic inspection. The acquisition of ultrasonic data was mostly automated in recent years, but the analysis of the collected data is still performed manually. This process is thus very expensive, inconsistent, and prone to human errors. An automated system would significantly increase the efficiency of analysis but the methods presented so far fail to generalize well on new cases and are not used in real-life inspection. Many of the similar data analysis problems were recently tackled by deep learning methods. This approach outperforms classical methods but requires lots of training data which is difficult to obtain in the NDE domain. In this work, we train a deep learning architecture EfficientDet to automatically detect defects from ultrasonic images. We showed how some of the hyperparameters can be tweaked in order to improve the detection of defects with extreme aspect ratios that are common in ultrasonic images. The proposed object detector was trained on the largest dataset of ultrasonic images that was so far seen in the literature. In order to collect the dataset, six steel blocks containing 68 defects were scanned with a phased array probe. More than 4000 VC-B-scans were acquired and used for training and evaluation of EfficientDet. The proposed model achieved 89.6% of mean average precision during 5-fold cross-validation which is a significant improvement compared to some similar methods that were previously used for this task. A detailed performance overview for each of the folds revealed that EfficientDet-D0 successfully detects all of the defects present in the inspected material.Four-dimensional cone-beam computed tomography (4D CBCT) has been developed to provide a sequence of phase-resolved reconstructions in image-guided radiation therapy. However, 4D CBCT images are degraded by severe streaking artifacts and noise because the phase-resolved image is an extremely sparse-view CT procedure wherein a few under-sampled projections are used for the reconstruction of each phase. Aiming at improving the overall quality of 4D CBCT images, we proposed two CNN models, named N-Net and CycN-Net, respectively, by fully excavating the inherent property of 4D CBCT. To be specific, the proposed N-Net incorporates the prior image reconstructed from entire projection data based on U-Net to boost the image quality for each phase-resolved image. Based on N-Net, a temporal correlation among the phase-resolved images is also considered by the proposed CycN-Net. Extensive experiments on both XCAT simulation data and real patient 4D CBCT datasets were carried out to verify the feasibility of the proposed CNNs. Both networks can effectively suppress streaking artifacts and noise while restoring the distinct features simultaneously, compared with the existing CNN models and two state-of-the-art iterative algorithms. VX-680 solubility dmso Moreover, the proposed method is robust in handling complicated tasks of various patient datasets and imaging devices, which implies its excellent generalization ability.Deep learning, particularly the generative model, has demonstrated tremendous potential to significantly speed up image reconstruction with reduced measurements recently. Rather than the existing generative models that often optimize the density priors, in this work, by taking advantage of the denoising score matching, homotopic gradients of generative density priors (HGGDP) are exploited for magnetic resonance imaging (MRI) reconstruction. More precisely, to tackle the low-dimensional manifold and low data density region issues in generative density prior, we estimate the target gradients in higher-dimensional space. We train a more powerful noise conditional score network by forming high-dimensional tensor as the network input at the training phase. More artificial noise is also injected in the embedding space. At the reconstruction stage, a homotopy method is employed to pursue the density prior, such as to boost the reconstruction performance. Experiment results implied the remarkable performance of HGGDP in terms of high reconstruction accuracy. Only 10% of the k-space data can still generate image of high quality as effectively as standard MRI reconstructions with the fully sampled data.Most of the real-world virtual reality (VR) content available today is captured and rendered from a fixed vantage point. The visual-vestibular conflict arising from the lack of head-motion parallax degrades the feeling of presence in the virtual environment and has been shown to induce nausea and visual discomfort. We present an end-to-end framework for VR with head-motion parallax for real-world scenes. To capture both horizontally and vertically separated perspectives, we use a camera rig with two vertically stacked rings of outward-facing cameras. The data from the rig are processed offline and stored into a compact intermediate representation, which is used to render novel views for a head-mounted display, in accordance with the viewer's head movements. We compare two promising intermediate representations-Stacked OmniStereo and Layered Depth Panoramas-and evaluate them in terms of objective image quality metrics and the occurrence of disocclusion holes in synthesized novel views.Human brain is a complex yet economically organized system, where a small portion of critical hub regions support the majority of brain functions. The identification of common hub nodes in a population of networks is often simplified as a voting procedure on the set of identified hub nodes across individual brain networks, which ignores the intrinsic data geometry and partially lacks the reproducible findings in neuroscience. Hence, we propose a first-ever group-wise hub identification method to identify hub nodes that are common across a population of individual brain networks. Specifically, the backbone of our method is to learn common graph embedding that can represent the majority of local topological profiles. By requiring orthogonality among the graph embedding vectors, each graph embedding as a data element is residing on the Grassmannian manifold. We present a novel Grassmannian manifold optimization scheme that allows us to find the common graph embeddings, which not only identify the most reliable hub nodes in each network but also yield a population-based common hub node map.