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Workshops on Image processing for the medicine, 2017

作者： Hammernik, Kerstin Würfl, Tobias Pock, Thomas Maier, Andreas Institute of Computer Graphics and Vision Graz University of Technology Austria Pattern Recognition Lab Friedrich-Alexander-University Germany Digital Safety and Security Department AIT Austrian Institute of Technology Austria

ISBN: (纸本)9783662543443

Limited-angle computed tomography suffers from missing data in the projection domain, which results in intensity inhomogeneities and streaking artifacts in the image domain. We address both challenges by a two-step deep learning architecture: First, we learn compensation weights that account for the missing data in the projection domain and correct for intensity changes. Second, we formulate an image restoration problem as a variational network to eliminate coherent streaking artifacts. We perform our experiments on realistic data and we achieve superior results for destreaking compared to state-of-the-art non-linear filtering methods in literature. We show that our approach eliminates the need for manual tuning and enables joint optimization of both correction schemes. © Springer-Verlag GmbH Deutschland 2017.

关键词： Image reconstruction

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Accurate camera registration in urban environments using high-level feature matching 28

Accurate camera registration in urban environments using hig...

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28th British Machine Vision Conference, BMVC 2017

作者： Armagan, Anil Hirzer, Martin Roth, Peter M. Lepetit, Vincent Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Laboratoire Bordelais de Recherche en Informatique Université de Bordeaux Bordeaux France

ISBN: (纸本)190172560X

We propose a method for accurate camera pose estimation in urban environments from single images and 2D maps made of the surrounding buildings’ outlines. Our approach bridges the gap between learning-based approaches and geometric approaches: We use recent semantic segmentation techniques for extracting the buildings’ edges and the façades’ normals in the images and minimal solvers [14] to compute the camera pose accurately and robustly. We propose two such minimal solvers: one based on three correspondences of buildings’ corners from the image and the 2D map and another one based on two corner correspondences plus one façade correspondence. We show on a challenging dataset that, compared to recent state-of-the-art [1], this approach is both, faster and more accurate. © 2017. The copyright of this document resides with its authors.

关键词： Bridge approaches

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Real-time panoramic tracking for event cameras

Real-time panoramic tracking for event cameras

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IEEE International Conference on Computational Photography (ICCP)

作者： Christian Reinbacher Gottfried Munda Thomas Pock Institute for Computer Graphics and Vision Graz University of Technology

ISBN: (纸本)9781509057467

Event cameras are a paradigm shift in camera technology. Instead of full frames, the sensor captures a sparse set of events caused by intensity changes. Since only the changes are transferred, those cameras are able to capture quick movements of objects in the scene or of the camera itself. In this work we propose a novel method to perform camera tracking of event cameras in a panoramic setting with three degrees of freedom. We propose a direct camera tracking formulation, similar to state-of-the-art in visual odometry. We show that the minimal information needed for simultaneous tracking and mapping is the spatial position of events, without using the appearance of the imaged scene point. We verify the robustness to fast camera movements and dynamic objects in the scene on a recently proposed dataset [18] and self-recorded sequences.

关键词： Cameras Tracking Robot vision systems Two dimensional displays computer vision Simultaneous localization and mapping Image reconstruction

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Scalable surface reconstruction from point clouds with extreme scale and density diversity

arXiv

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arXiv 2017年

作者： Mostegel, Christian Prettenthaler, Rudolf Fraundorfer, Friedrich Bischof, Horst Institute for Computer Graphics and Vision Graz University of Technology

In this paper we present a scalable approach for robustly computing a 3D surface mesh from multi-scale multi-view stereo point clouds that can handle extreme jumps of point density (in our experiments three orders of magnitude). The backbone of our approach is a combination of octree data partitioning, local Delaunay tetrahedralization and graph cut optimization. Graph cut optimization is used twice, once to extract surface hypotheses from local Delaunay tetrahedralizations and once to merge overlapping surface hypotheses even when the local tetrahedralizations do not share the same topology. This formulation allows us to obtain a constant memory consumption per sub-problem while at the same time retaining the density independent interpolation properties of the Delaunay-based optimization. On multiple public datasets, we demonstrate that our approach is highly competitive with the state-of-the-art in terms of accuracy, completeness and outlier resilience. Further, we demonstrate the multi-scale potential of our approach by processing a newly recorded dataset with 2 billion points and a point density variation of more than four orders of magnitude-requiring less than 9GB of RAM per process. Copyright © 2017, The Authors. All rights reserved.

关键词： Random access storage

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ALCN: Adaptive local contrast normalization for robust object detection and 3D pose estimation 28

ALCN: Adaptive local contrast normalization for robust objec...

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28th British Machine Vision Conference, BMVC 2017

作者： Rad, Mahdi Roth, Peter M. Lepetit, Vincent Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Laboratoire Bordelais de Recherche en Informatique Université de Bordeaux Bordeaux France

ISBN: (纸本)190172560X

To be robust to illumination changes when detecting objects in images, the current trend is to train a Deep Network with training images captured under many different lighting conditions. Unfortunately, creating such a training set is very cumbersome, or sometimes even impossible, for some applications such as 3D pose estimation of specific objects, which is the application we focus on in this paper. We therefore propose a novel illumination normalization method that lets us learn to detect objects and estimate their 3D pose under challenging illumination conditions from very few training samples. Our key insight is that normalization parameters should adapt to the input image. In particular, we realized this via a Convolutional Neural Network trained to predict the parameters of a generalization of the Difference-of-Gaussians method. We show that our method significantly outperforms standard normalization methods and demonstrate it on two challenging 3D detection and pose estimation problems. © 2017. The copyright of this document resides with its authors.

关键词： Object detection

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Rapid, automated post-event image classification and documentation 7

Rapid, automated post-event image classification and documen...

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7th International Conference on Advances in Experimental Structural Engineering, AESE 2017

作者： Yeum, Chul Min Dyke, Shirley J. Benes, Bedrich Hacker, Thomas Ramirez, Julio Lund, Alana Pujol, Santiago Lyles School of Civil Engineering Purdue University West LafayetteIN47907 United States Computer Graphics Technology Purdue University West LafayetteIN47907 United States Computer and Information Technology Purdue University West LafayetteIN47907 United States

ISBN: (纸本)9788885701021

Reconnaissance teams collect large volumes of perishable data after a natural disaster that are related to the condition of the buildings and other infrastructure. Each event is an opportunity to evaluate the performance of our structures under circumstances that cannot entirely be reproduced in the laboratory or numerical simulation. In the field, engineers typically prefer to record such information through images. For each building, images readily document the visual appearance of damage to structures and their components. Each team follows a similar procedure that includes taking views of the structure, from both outside and inside, using various distances and angles. During this process, engineers frequently incorporate metadata in the form of images, such as structural drawings, GPS devices, watches, and even measurements (e.g. an image of a structural column with a measuring tape). Large quantities of images with a wide variety of content are collected within a short period, and their timely organization and documentation are important. Engineers need to generate accurate and rich descriptions of the images before the details are forgotten. To distil such information in an efficient and rapid manner, we developed an automated approach that uses computer vision techniques for classifying and organizing the large volume of images collected from each building. Deep convolutional neural network (CNN) algorithms are successfully implemented to extract robust features of key visual contents in the images. Thsi capability is demonstrated using data collected by various reconnaissance teams from buildings damaged during past earthquakes. A schema is developed based on the real needs of field teams examining buildings, with several different categories and information defined and used for annotating the images, thus supporting organization of the data that parallels the procedure followed by the engineer in the field. A significant volume of these images from past ear

关键词： Building reconnaissance Convolutional neural networks Image classification Post-disaster evaluation

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Real-time panoramic tracking for event cameras

arXiv

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arXiv 2017年

作者： Reinbacher, Christian Munda, Gottfried Pock, Thomas Institute for Computer Graphics and Vision Graz University of Technology Austria

关键词： Cameras

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View-Aware Image Object Compositing and Synthesis from Multiple Sources

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Journal of computer Science & technology 2016年第3期31卷 463-478页

作者： Xiang Chen Wei-Wei Xu Sai-Kit Yeung Kun Zhou State Key Laboratory of Computer Aided Design and Computer Graphics Zhejiang University Hangzhou 310058 China Vision Graphics and Computational Design Group Singapore University of Technology and Design Singapore 487372 Singapore

Image compositing is widely used to combine visual elements from separate source images into a single image. Although recent image compositing techniques are capable of achieving smooth blending of the visual elements from different sources, most of them implicitly assume the source images are taken in the same viewpoint. In this paper, we present an approach to compositing novel image objects from multiple source images which have different viewpoints. Our key idea is to construct 3D proxies for meaningful components of the source image objects, and use these 3D component proxies to warp and seamlessly merge components together in the same viewpoint. To realize this idea, we introduce a coordinate- frame based single-view camera calibration algorithm to handle general types of image objects, a structure-aware cuboid optimization algorithm to get the cuboid proxies for image object components with correct structure relationship, and finally a 3D-proxy transformation guided image warping algorithm to stitch object components. We further describe a novel application based on this compositing approach to automatically synthesize a large number of image objects from a set of exemplars. Experimental results show that our compositing approach can be applied to a variety of image objects, such as chairs, cups, lamps, and robots, and the synthesis application can create novel image objects with significant shape and style variations from a small set of exemplars.

关键词： image cloning 3D proxy probabilistic modeling data-driven method

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POLCOVID: a multicenter multiclass chest X-ray database (Poland, 2020-2021)

arXiv

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arXiv 2022年

作者： Suwalska, Aleksandra Tobiasz, Joanna Prazuch, Wojciech Socha, Marek Foszner, Pawel Piotrowski, Damian Gruszczynska, Katarzyna Sliwinska, Magdalena Walecki, Jerzy Popiela, Tadeusz Przybylski, Grzegorz Nowak, Mateusz Fiedor, Piotr Pawlowska, Malgorzata Flisiak, Robert Simon, Krzysztof Zapolska, Gabriela Gizycka, Barbara Szurowska, Edyta Marczyk, Michal Cieszanowski, Andrzej Polanska, Joanna Oronowicz-Jaskowiak, Agnieszka Golebiewski, Bogumil Sliwinska, Magdalena Rataj, Mateusz Chmielarz, Przemyslaw Tur, Adrianna Drabik, Grzegorz Kozub, Justyna Kozanecka, Anna Hildebrandt, Sebastian Krutul-Walenciej, Katarzyna Baron, Jan Jaroszewicz, Jerzy Walecki, Jerzy Wasilewski, Piotr Mazur, Samuel Klaude, Krzysztof Rataj, Katarzyna Golębiewski, Bogumil Pawlowska, Malgorzata Rabiko, Piotr Rajewski, Pawel Gizycka, Barbara Blewaska, Piotr Sznajder, Katarzyna Plesniak, Robert Department of Data Science and Engineering Silesian University of Technology Gliwice Poland Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Gliwice Poland Department of Infectious Diseases and Hepatology Medical University of Silesia Katowice Poland Department of Radiology and Nuclear Medicine Medical University of Silesia Katowice Poland Department of Diagnostic Imaging Voivodship Specialist Hospital Wroclaw Poland Department of Diagnostic Radiology Central Clinical Hospital The Ministry of Internal Affairs and Administration Warsaw Poland Department of Radiology Jagiellonian University Medical College Krakow Poland Department of Lung Diseases Cancer and Tuberculosis Kujawsko-Pomorskie Pulmonology Center Bydgoszcz Poland Department of Radiology Silesian Hospital Cieszyn Poland Department of General and Transplantation Surgery Medical University of Warsaw Warsaw Poland Department of Infectious Diseases and Hepatology Collegium Medicum in Bydgoszcz Nicolaus Copernicus University Torun Poland Department of Infectious Diseases and Hepatology Medical University of Bialystok Bialystok Poland Department of Infectious Diseases and Hepatology Wroclaw Medical University Wroclaw Poland Department of Radiology Czerniakowski Hospital Warsaw Poland Department of Imaging Diagnostics MEGREZ Hospital Tychy Poland 2nd Department of Radiology Medical University of Gdansk Poland Yale Cancer Center Yale School of Medicine New HavenCT United States Department of Radiology I The Maria Sklodowska-Curie National Research Institute of Oncology Warsaw Poland The Maria Sklodowska-Curie National Research Institute of Oncology Warsaw Poland Voivodship Specialist Hospital Wroclaw Poland Prognostic Specialist Clinic Knurow Poland Collegium Medicum of the Jagiellonian University Krakow Poland Medical University of Silesia Katowice Poland Central Clinical Hospital of the Ministry of Interior in Warsaw Warsaw Poland Collegium Medicum

The outbreak of the SARS-CoV-2 pandemic has put healthcare systems worldwide to their limits, resulting in increased waiting time for diagnosis and required medical assistance. With chest radiographs (CXR) being one of the most common COVID-19 diagnosis methods, many artificial intelligence tools for image-based COVID-19 detection have been developed, often trained on a small number of images from COVID-19-positive patients. Thus, the need for high-quality and well-annotated CXR image databases increased. This paper introduces POLCOVID dataset, containing chest X-ray (CXR) images of patients with COVID-19 or other-type pneumonia, and healthy individuals gathered from 15 Polish hospitals. The original radiographs are accompanied by the preprocessed images limited to the lung area and the corresponding lung masks obtained with the segmentation model. Moreover, the manually created lung masks are provided for a part of POLCOVID dataset and the other four publicly available CXR image collections. POLCOVID dataset can help in pneumonia or COVID-19 diagnosis, while the set of matched images and lung masks may serve for the development of lung segmentation solutions. © 2022, CC BY.

关键词： COVID-19

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Robot Localisation and 3D Position Estimation Using a Free-Moving Camera and Cascaded Convolutional Neural Networks

Robot Localisation and 3D Position Estimation Using a Free-M...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Justinas Miseikis Patrick Knobelreiter Inka Brijacak Saeed Yahyanejad Kyrre Glette Ole Jakob Elle Jim Torresen Department of Informatics Universitv of Oslo Oslo Norway Institute of computer Graphics and Vision Graz University of Technology Graz Austria Joanneum Research - Robotics Klagenfurt am Worthersee Austria The Intervention Centre Oslo University Hospital Oslo Norway

Many works in collaborative robotics and human-robot interaction focuses on identifying and predicting human behaviour while considering the information about the robot itself as given. This can be the case when sensors and the robot are calibrated in relation to each other and often the reconfiguration of the system is not possible, or extra manual work is required. We present a deep learning based approach to remove the constraint of having the need for the robot and the vision sensor to be fixed and calibrated in relation to each other. The system learns the visual cues of the robot body and is able to localise it, as well as estimate the position of robot joints in 3D space by just using a 2D color image. The method uses a cascaded convolutional neural network, and we present the structure of the network, describe our own collected dataset, explain the network training and achieved results. A fully trained system shows promising results in providing an accurate mask of where the robot is located and a good estimate of its joints positions in 3D. The accuracy is not good enough for visual servoing applications yet, however, it can be sufficient for general safety and some collaborative tasks not requiring very high precision. The main benefit of our method is the possibility of the vision sensor to move freely. This allows it to be mounted on moving objects, for example, a body of the person or a mobile robot working in the same environment as the robots are operating in.

关键词： Robot kinematics Robot sensing systems Collision avoidance Vision sensors Cameras Three-dimensional displays

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