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arXiv 2020年

作者： Li, Jianning Pepe, Antonio Gsaxner, Christina Egger, Jan Institute of Computer Graphics and Vision Graz University of Technology Computer Algorithms for Medicine Laboratory Graz Austria Department of Oral and Maxillofacial Surgery Medical University of Graz

We introduce a fully automatic system for cranial implant design, a common task in cranioplasty operations. The system is currently integrated in Studierfenster (http://***/), an online, cloud-based medical image processing platform for medical imaging applications. Enhanced by deep learning algorithms, the system automatically restores the missing part of a skull (i.e., skull shape completion) and generates the desired implant by subtracting the defective skull from the completed skull. The generated implant can be downloaded in the STereoLithography (.stl) format directly via the browser interface of the system. The implant model can then be sent to a 3D printer for in loco implant manufacturing. Furthermore, thanks to the standard format, the user can thereafter load the model into another application for post-processing whenever necessary. Such an automatic cranial implant design system can be integrated into the clinical practice to improve the current routine for surgeries related to skull defect repair (e.g., cranioplasty). Our system, although currently intended for educational and research use only, can be seen as an application of additive manufacturing for fast, patient-specific implant design. Copyright © 2020, The Authors. All rights reserved.

关键词： 3D printers

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Bilevel learning of regularization models and their discretization for image deblurring and super-resolution

arXiv

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arXiv 2023年

作者： Bubba, Tatiana A. Calatroni, Luca Catozzi, Ambra Crisci, Serena Pock, Thomas Pragliola, Monica Rautio, Siiri Riccio, Danilo Sebastiani, Andrea Department of Mathematical Sciences University of Bath United Kingdom CNRS UCA INRIA Laboratoire I3S France Department of Mathematical Physical and Computer Sciences University of Parma Italy Department of Mathematics and Physics University of Campania "Luigi Vanvitelli" Italy Institute of Computer Graphics and Vision Graz University of Technology Austria Department of Mathematics and Applications University of Naples Federico II Italy Department of Mathematics and Statistics University of Helsinki Finland School of Mathematical Sciences Queen Mary University of London United Kingdom Department of Mathematics University of Bologna Italy

Bilevel learning is a powerful optimization technique that has extensively been employed in recent years to bridge the world of model-driven variational approaches with data-driven methods. Upon suitable parametrization of the desired quantities of interest (e.g., regularization terms or discretization filters), such approach computes optimal parameter values by solving a nested optimization problem where the variational model acts as a constraint. In this work, we consider two different use cases of bilevel learning for the problem of image restoration. First, we focus on learning scalar weights and convolutional filters defining a Field of Experts regularizer to restore natural images degraded by blur and noise. For improving the practical performance, the lower-level problem is solved by means of a gradient descent scheme combined with a line-search strategy based on the Barzilai-Borwein rule. As a second application, the bilevel setup is employed for learning a discretization of the popular total variation regularizer for solving image restoration problems (in particular, deblurring and super-resolution). Numerical results show the effectiveness of the approach and their generalization to multiple *** Codes 65K10 Copyright © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

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Map-Repair: Deep Cadastre Maps Alignment and Temporal Inconsistencies Fix in Satellite Images

Map-Repair: Deep Cadastre Maps Alignment and Temporal Incons...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Stefano Zorzi Ksenia Bittner Friedrich Fraundorfer Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Remote Sensing Technology Institute German Aerospace Center (DLR) Wessling Germany

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

In the fast developing countries it is hard to trace new buildings construction or old structures destruction and, as a result, to keep the up-to-date cadastre maps. Moreover, due to the complexity of urban regions or inconsistency of data used for cadastre maps extraction, the errors in form of misalignment is a common problem. In this work, we propose an end-to-end deep learning approach which is able to solve inconsistencies between the input intensity image and the available building footprints by correcting label noises and, at the same time, misalignments if needed. The obtained results demonstrate the robustness of the proposed method to even severely misaligned examples that makes it potentially suitable for real applications, like OpenStreetMap correction.

关键词： Annotations Buildings Image segmentation Noise measurement Generators Training Satellites

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Machine-learning guided differentiation between photoplethysmography waveforms of supraventricular and ventricular origin

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EP Europace 2024年第SUPPLEMENT_1期26卷

作者： Manninger, M Lercher, I Hermans, A N L Isaksen, J L Prassl, A J Zirlik, A Vernooy, K Chaldoupi, S M Luermans, J Ter Bekke, R M A Kanters, J K Plank, G Scherr, D Pock, T Linz, D Medical University of Graz Department of Cardiology Graz Austria Graz University of Technology Institute of Computer Graphics and Vision Graz Austria Maastricht University Medical Centre (MUMC) Department of Cardiology Maastricht Netherlands (The) University of Copenhagen Department of Biomedical Sciences Faculty of Health and Medical Sciences Copenhagen Denmark Medical University of Graz Gottfried Schatz Research Centre Division of Biophysics Graz Austria

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IRIS: Interactive real-time feedback image segmentation with deep learning

IRIS: Interactive real-time feedback image segmentation with...

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Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Pepe, Antonio Schussnig, Richard Li, Jianning Gsaxner, Christina Chen, Xiaojun Fries, Thomas-Peter Egger, Jan Stanford University School of Medicine Department of Radiology 300 Pasteur Dr StanfordCA United States Graz University of Technology Institute of Computer Graphics and Vision Inffeldgasse 16c/II GrazA-8010 Austria Computer Algorithms for Medicine Laboratory GrazA-8010 Austria Graz University of Technology Institute of Structural Analysis Lessingstraße 25/II GrazA-8010 Austria Medical University of Graz Department of Oral and Maxillofacial Surgery Auenbruggerplatz 5/1 GrazA-8036 Austria Shanghai Jiao Tong University School of Mechanical Engineering 800 Dong Chuan Road Shanghai200240 China

ISBN: (纸本)9781510634015

Volumetric examinations of the aorta are nowadays of crucial importance for the management of critical pathologies such as aortic dissection, aortic aneurism, and other pathologies, which affect the morphology of the artery. These examinations usually begin with the acquisition of a Computed Tomography Angiography (CTA) scan from the patient, which is later on postprocessed to reconstruct the 3D geometry of the aorta. The first postprocessing step is referred to as segmentation. Different algorithms have been suggested for the segmentation of the aorta;including interactive methods, as well as fully automatic methods. Interactive methods need to be fine-tuned on each single CTA scan and result in longer duration of the process, whereas fully automatic methods require the possession of a large amount of labeled training data. In this work, we introduce a hybrid approach by combining a deep learning method with a consolidated interaction technique. In particular, we trained a 2D and a 3D U-Net on a limited number of patches extracted from 25 labeled CTA scans. Afterwards, we use an interactive approach, which consists in defining a region of interest (ROI) by just placing a seed point. This seed point is later used as the center of a 2D or 3D patch to be fed to the 2D or 3D U-Net, respectively. Due to the low content variation of these patches, this method allows to correctly segment the ROIs without the need for parameter tuning for each dataset and with a smaller training dataset, requiring the same minimal interaction as state-of-the-art interactive methods. Later on, the new segmented CTA scans can be further used to train a convolutional network for a fully automatic approach. © 2020 SPIE

关键词： Angiography

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FRSM: A Novel Fault-Tolerant Approach for Redundant-Path-Enabled Service Migration in Mobile Edge Computing 1

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29th International Conference on Web Services, ICWS 2022 Held as Part of the Services Conference Federation, SCF 2022

作者： Zhao, Jiale Dai, Mengxuan Xia, Yunni Ma, Yong He, Meibin Peng, Kai Li, Jianqi Li, Fan Fu, Xiaodong School of Computer Chongqing University Chongqing400030 China School of Computer and Information Engineering Jiangxi Normal University Nanchang330022 China Department of Big Data and AI of Jiangxi Telecom Nanchang330029 China Engineering College Huaqiao University Quanzhou362021 China Global Energy Interconnection Research Institute Co. Ltd. Beijing102209 China Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University Chengdu610065 China Data Science Research Center Faculty of Science Kunming University of Science and Technology Kunming650031 China

ISBN: (数字)9783031235795

ISBN: (纸本)9783031235788

Mobile Edge Computing (MEC) provides users with low-latency, highly responsive services by deploying Edge Servers (ESs) near applications. MEC allows any edge-hosted application or service to be migrated between different edge resource providers without being locked into a single provider. Nevertheless, due to its complexity and dynamics, the real edge computing environment is prone to errors and failures, reducing the reliability of edge service migration. This paper proposes a novel fault-tolerant method for redundant path service migration. The method utilizes sliding-window-based model and identifies a set of service migration paths, enabling the evaluation of the time-varying failure rate of ESs. The method combines resubmission and replication mechanisms and decides the edge service migration scheme by selecting multiple redundant migration paths. We also conduct extensive simulations and show that our proposed method outperforms traditional solutions in several metrics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile edge computing

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Efficient dataflow modeling of peripheral encoding in the human visual system

arXiv

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arXiv 2021年

作者： Brown, Rachel Dutell, Vasha Walter, Bruce Rosenholtz, Ruth Shirley, Peter McGuire, Morgan Luebke, David NVIDIA Research Vision Science UC Berkeley Computer Graphics Cornell University Brain and Cognitive Sciences CSAIL Massachusetts Institute of Technology NVIDIA Research

computer graphics seeks to deliver compelling images, generated within a computing budget, targeted at a specific display device, and ultimately viewed by an individual user. The foveated nature of human vision offers an opportunity to efficiently allocate computation and compression to appropriate areas of the viewer's visual field, especially with the rise of high resolution and wide field-of-view display devices. However, while the ongoing study of foveal vision is advanced, much less is known about how humans process imagery in the periphery of their vision-which comprises, at any given moment, the vast majority of the pixels in the image. We advance computational models for peripheral vision aimed toward their eventual use in computer graphics. In particular, we present a dataflow computational model of peripheral encoding that is more efficient than prior pooling-based methods and more compact than contrast sensitivity-based methods. Further, we account for the explicit encoding of "end stopped" features in the image, which was missing from previous methods. Finally, we evaluate our model in the context of perception of textures in the periphery. Our improved peripheral encoding may simplify development and testing of more sophisticated, complete models in more robust and realistic settings relevant to computer graphics. © 2021, CC BY.

关键词： Budget control

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General 3D Room Layout from a Single View by Render-and-Compare

arXiv

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arXiv 2020年

作者： Stekovic, Sinisa Fraundorfer, Friedrich Lepetit, Vincent Institute for Computer Graphics and Vision Graz University of Technology Graz Austria Université Paris-Est École des Ponts ParisTech Paris France

We present a novel method to reconstruct the 3D layout of a room—walls, floors, ceilings—from a single perspective view, even for the case of general configurations. This input view can consist of a color image only, but considering a depth map will result in a more accurate reconstruction. Our approach is based on solving a constrained discrete optimization problem, which selects the polygons which are part of the layout from a large set of potential polygons. In order to deal with occlusions between components of the layout, which is a problem ignored by previous works, we introduce an analysis-by-synthesis method to iteratively refine the 3D layout estimate. To the best of our knowledge, our method is the first that can estimate a layout in such general conditions from a single view. We additionally introduce a new annotation dataset made of 91 images from the ScanNet dataset and several metrics, in order to evaluate our results quantitatively. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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Casting Geometric Constraints in Semantic Segmentation as Semi-Supervised Learning

Casting Geometric Constraints in Semantic Segmentation as Se...

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IEEE Workshop on Applications of computer vision (WACV)

作者： Sinisa Stekovic Friedrich Fraundorfer Vincent Lepetit Institute for Computer Graphics and Vision Graz University of Technology Graz Austria École des Ponts ParisTech Université Paris-Est Paris France

ISBN: (数字)9781728165530

ISBN: (纸本)9781728165547

We propose a simple yet effective method to learn to segment new indoor scenes from video frames: State-of- the-art methods trained on one dataset, even as large as the SUNRGB-D dataset, can perform poorly when applied to images that are not part of the dataset, because of the dataset bias, a common phenomenon in computer vision. To make semantic segmentation more useful in practice, one can exploit geometric constraints. Our main contribution is to show that these constraints can be cast conveniently as semi-supervised terms, which enforce the fact that the same class should be predicted for the projections of the same 3D location in different images. This is interesting as we can exploit general existing techniques de- veloped for semi-supervised learning to efficiently incorporate the constraints. We show that this approach can efficiently and accurately learn to segment target sequences of ScanNet and our own target sequences using only annotations from SUNRGB-D, and geometric relations between the video frames of target sequences.

关键词： Image segmentation Semantics Predictive models Semisupervised learning Task analysis Training Three-dimensional displays

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AUTOMATIC MUCK PILE CHARACTERIZATION from UAV IMAGES 4

AUTOMATIC MUCK PILE CHARACTERIZATION from UAV IMAGES

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4th ISPRS Geospatial Week 2019

作者： Schenk, F. Tscharf, A. Mayer, G. Fraundorfer, F. Institute of Computer Graphics and Vision Graz University of Technology Austria Mining Engineering and Mineral Economics Montanuniversitaet Leoben Austria

In open pit mining it is essential for processing and production scheduling to receive fast and accurate information about the fragmentation of a muck pile after a blast. In this work, we propose a novel machine-learning method that characterizes the muck pile directly from UAV images. In contrast to state-of-the-art approaches, that require heavy user interaction, expert knowledge and careful threshold settings, our method works fully automatically. We compute segmentation masks, bounding boxes and confidence values for each individual fragment in the muck pile on multiple scales to generate a globally consistent segmentation. Additionally, we recorded lab and real-world images to generate our own dataset for training the network. Our method shows very promising quantitative and qualitative results in all our experiments. Further, the results clearly indicate that our method generalizes to previously unseen data. © Authors 2019.

关键词： Unmanned aerial vehicles (UAV)

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