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arXiv 2019年

作者： Wallner, Jürgen Mischak, Irene Egger, Jan Department of Oral and Maxillofacial Surgery Medical University of Graz Auenbruggerplatz 5/1 Graz8036 Austria Computer Algorithms for Medicine Laboratory Graz Austria University Clinic of Dental Medicine and Oral Health Medical University of Graz Billrothgasse 4 Graz8010 Austria Institute for Computer Graphics and Vision Graz University of Technology Inffeldgasse 16c/II Graz8010 Austria

Image-based algorithmic software segmentation is an increasingly important topic in many medical fields. Algorithmic segmentation is used for medical three-dimensional visualization, diagnosis or treatment support, especially in complex medical cases. However, accessible medical databases are limited, and valid medical ground truth databases for the evaluation of algorithms are rare and usually comprise only a few images. Inaccuracy or invalidity of medical ground truth data and image-based artefacts also limit the creation of such databases, which is especially relevant for CT data sets of the maxillomandibular complex. This contribution provides a unique and accessible data set of the complete mandible, including 20 valid ground truth segmentation models originating from 10 CT scans from clinical practice without artefacts or faulty slices. From each CT scan, two 3D ground truth models were created by clinical experts through independent manual slice-by-slice segmentation, and the models were statistically compared to prove their validity. These data could be used to conduct serial image studies of the human mandible, evaluating segmentation algorithms and developing adequate image tools. Copyright © 2019, The Authors. All rights reserved.

关键词： computerized tomography

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Pixelvae: A latent variable model for natural images 5

Pixelvae: A latent variable model for natural images

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5th International Conference on Learning Representations, ICLR 2017

作者： Gulrajani, Ishaan Kumar, Kundan Ahmed, Faruk Taiga, Adrien Ali Visin, Francesco Vazquez, David Courville, Aaron Montreal Institute for Learning Algorithms Université de Montréal Canada Department of Computer Science and Engineering IIT Kanpur India CentraleSupélec France Computer Vision Center Universitat Autonoma de Barcelona Spain Politecnico di Milano Italy CIFAR

Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and model global structure well but have difficulty capturing small details. PixelCNN models details very well, but lacks a latent code and is difficult to scale for capturing large structures. We present PixelVAE, a VAE model with an autoregressive decoder based on PixelCNN. Our model requires very few expensive autoregressive layers compared to PixelCNN and learns latent codes that are more compressed than a standard VAE while still capturing most non-trivial structure. Finally, we extend our model to a hierarchy of latent variables at different scales. Our model achieves state-of-the-art performance on binarized MNIST, competitive performance on 64 × 64 ImageNet, and high-quality samples on the LSUN bedrooms dataset. © ICLR 2019 - Conference Track Proceedings. All rights reserved.

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vision-based Unmanned Aerial Vehicle detection and tracking for sense and avoid systems

Vision-based Unmanned Aerial Vehicle detection and tracking ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Krishna Raj Sapkota Steven Roelofsen Artem Rozantsev Vincent Lepetit Denis Gillet Pascal Fua Alcherio Martinoli Computer Vision Laboratory School of Communication and Computer Sciences école Polytechnique Fédérale de Lausanne (EPFL) Switzerland Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering EPFL Switzerland Institute for Computer Graphics and Vision TU Graz Austria Coordination and Interaction System Group School of Engineering EPFL Switzerland

ISBN: (纸本)9781509037636

We propose an approach for on-line detection of small Unmanned Aerial Vehicles (UAVs) and estimation of their relative positions and velocities in the 3D environment from a single moving camera in the context of sense and avoid systems. This problem is challenging both from a detection point of view, as there are no markers on the targets available, and from a tracking perspective, due to misdetection and false positives. Furthermore, the methods need to be computationally light, despite the complexity of computer vision algorithms, to be used on UAVs with limited payload. To address these issues we propose a multi-staged framework that incorporates fast object detection using an AdaBoost-based approach, coupled with an on-line visual-based tracking algorithm and a recent sensor fusion and state estimation method. Our framework allows for achieving real-time performance with accurate object detection and tracking without any need of markers and customized, high-performing hardware resources.

关键词： Target tracking Object detection Collision avoidance Cameras Visualization Detectors

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On the Evolution of Fingertip Grasping Manifolds

On the Evolution of Fingertip Grasping Manifolds

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IEEE International Conference on Robotics and Automation

作者： K. Hang J. A. Haustein M. Li A. Billard C. Smith D. Kragic the Computer Vision and Active Perception Lab CAS CSC at KTH Royal Institute of Technology Stockholm Sweden the Learning Algorithms and Systems Laboratory (LASA) at école Polytechnique Fédérale de Lausanne (EPFL) Switzerland

ISBN: (纸本)9781467380270

Efficient and accurate planning of fingertip grasps is essential for dexterous in-hand manipulation. In this work, we present a system for fingertip grasp planning that incrementally learns a heuristic for hand reachability and multi-fingered inverse kinematics. The system consists of an online execution module and an offline optimization module. During execution the system plans and executes fingertip grasps using Canny's grasp quality metric and a learned random forest based hand reachability heuristic. In the offline module, this heuristic is improved based on a grasping manifold that is incrementally learned from the experiences collected during execution. The system is evaluated both in simulation and on a Schunk-SDH dexterous hand mounted on a KUKA-KR5 arm. We show that, as the grasping manifold is adapted to the system's experiences, the heuristic becomes more accurate, which results in an improved performance of the execution module. The improvement is not only observed for experienced objects, but also for previously unknown objects of similar sizes.

关键词： Evolution Fingertip Grasping

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Classification of gothic and baroque architectural elements

Classification of gothic and baroque architectural elements

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2012 19th International Conference on Systems, Signals and Image Processing, IWSSIP 2012

作者： Shalunts, Gayane Haxhimusa, Yll Sablatnig, Robert Institute of Computer Aided Automation Computer Vision Lab. Vienna University of Technology Austria Institute of Computer Graphics and Algorithms Pattern Recognition and Image Processing Lab. Vienna University of Technology Austria

ISBN: (纸本)9783200023284

Architectural elements are the components and details of buildings. Their unique set, combination, design, construction technique form the architectural style of buildings. Building facade classification by architectural styles is viewed as a task of classifying separate architectural structural elements. In the scope of building facade architectural style classification the current paper targets the problem of classification of Gothic and Baroque architectural elements called tracery, pediment and balustrade. Since certain gradient directions dominate on the shape of each architectural element, discrimination between dominating gradients means classification of architectural elements and thus architectural styles. We use local features to describe gradient directions. Our approach is based on clustering and learning of local features and yields a high classification rate. © 2012 Institute of Telecommunica.

关键词： Classification (of information)

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Learning Coupled Dynamical Systems from human demonstration for robotic eye-arm-hand coordination

Learning Coupled Dynamical Systems from human demonstration ...

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IEEE-RAS International Conference on Humanoid Robots

作者： Luka Lukic José Santos-Victor Aude Billard Learning Algorithms and Systems Laboratory (LASA) EPFL Switzerland Computer Vision Laboratory (VISLAB) IST Portugal

Efficient, adaptive and reliable visuomotor control system is crucial to enable robots to display flexibility in the face of changes in the environment. This paper takes inspiration in human eye-arm-hand coordination pattern to develop an equivalently robust robot controller. We recorded gaze, arm, hand, and trunk data from human subjects in reaching and grasping scenarios with/without obstacle in the workspace. An eye-arm-hand controller is developed, based on our extension of Coupled Dynamical Systems (CDS). We exploit the time-invariant properties of the CDS to allow fast adaptation to spatial and temporal perturbations during task completion. CDS global stability guarantees that the eye, the arm and the hand will reach the target in retinal, operational and grasp space respectively. When facing perturbations, the system can re-plan its actions almost instantly, without the need for an additional planning module. Coupling profiles for eye-arm and arm-hand systems can be modulated allowing to adjust the behavior of each slave system with respect to control signals flowing from the corresponding master system. We show how the CDS eye-arm-hand control framework can be used to handle the presence of obstacles in the workspace. The eye-arm-hand controller is validated in a series of experiments conducted with the iCub robot.

关键词： Robot kinematics Couplings Heuristic algorithms

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Classification of gothic and baroque architectural elements

Classification of gothic and baroque architectural elements

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International Conference on Systems, Signals and Image Processing, IWSSIP

作者： Gayane Shalunts Yll Haxhimusa Robert Sablatnig Institute of Computer Aided Automation Computer Vision Laboratory University of Technology Vienna Austria Institute of Computer Graphics and Algorithms Pattern Recognition and Image Processing Lab University of Technology Vienna Austria

关键词： Shape Three dimensional displays Visualization computer vision Histograms Windows

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Architectural style classification of building facade windows

Architectural style classification of building facade window...

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7th International Symposium on Visual Computing, ISVC 2011

作者： Shalunts, Gayane Haxhimusa, Yll Sablatnig, Robert Institute of Computer Aided Automation Computer Vision Lab. Vienna University of Technology Austria Institute of Computer Graphics and Algorithms Pattern Recongition and Image Processing Lab. Vienna University of Technology Austria

Building facade classification by architectural styles allows categorization of large databases of building images into semantic categories belonging to certain historic periods, regions and cultural influences. Image databases sorted by architectural styles permit effective and fast image search for the purposes of content-based image retrieval, 3D reconstruction, 3D city-modeling, virtual tourism and indexing of cultural heritage buildings. Building facade classification is viewed as a task of classifying separate architectural structural elements, like windows, domes, towers, columns, etc, as every architectural style applies certain rules and characteristic forms for the design and construction of the structural parts mentioned. In the context of building facade architectural style classification the current paper objective is to classify the architectural style of facade windows. Typical windows belonging to Romanesque, Gothic and Renaissance/Baroque European main architectural periods are classified. The approach is based on clustering and learning of local features, applying intelligence that architects use to classify windows of the mentioned architectural styles in the training stage. © 2011 Springer-Verlag.

关键词： Semantics

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MICAI 2010 Organization and Conference Committee

Proceedings of Special Session - 9th Mexican International C...

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Proceedings of Special Session - 9th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence and Applications, MICAI 2010 2010年 viii页

作者： Reyes-García, Carlos Alberto Sidorov, Grigori Hernández-Aguirre, Arturo Arroyo, Gustavo Murrieta, Rafael Gonzalez, Jesus A. Gonzalez, Miguel Herrera, Oscar Peña, Alejandro Espinoza, Félix A. Castro Cansino, Joel Suárez Galicia-Haro, Sofia N. Koeppen, Mario Reyes-García, Carlos A. Monroy, Raul Gelbukh, Alexander Mezura-Montes, Efrén Leguizamón, Guillermo Ramírez-Manzanares, Alonso Castillo, Oscar Fuentes, Olac Sánchez, Gildardo Natural Language Processing Mexico Machine Learning and Pattern Recognition Mexico Hybrid Intelligent Systems and Neural Networks Mexico Logic Reasoning Ontologies Knowledge Mgmt. Knowledge-Based Syst. Multi-agent Syst. Mexico Data Mining Mexico Intelligent Tutoring Systems Mexico Evolutionary Algorithms and Other Naturally Inspired Algorithms Mexico Computer Vision and Image Processing Mexico Fuzzy Logic Uncertainty and Probabilistic Reasoning Mexico Bioinformatics and Medical Applications Mexico Robotics Planning and Scheduling Mexico

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Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo

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ACM Computing Surveys 1000年

作者： Yuan Jin Antonio Pepe Jianning Li Christina Gsaxner Yuxuan Chen Behrus Puladi Fen-hua Zhao Kelsey Pomykala Jens Kleesiek Alejandro Frangi Jan Egger Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Research Centre for Frontier Fundamental Studies Zhejiang Lab Hangzhou China Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Institute for Artificial Intelligence in Medicine Essen University Hospital (AöR) Essen Germany Zhejiang Laboratory of Philosophy and Social Sciences - Laboratory of Intelligent Society and Governance Zhejiang Lab Hangzhou China Institute of Medical Informatics RWTH Aachen University Aachen Germany Wenzhou Medical University Affiliated Dongyang Hospital Dongyang China Cancer Research Center Cologne Essen Essen Germany German Cancer Consortium Heidelberg Germany Christabel Pankhurst Institute The University of Manchester Manchester United Kingdom of Great Britain and Northern Ireland School of Health Sciences The University of Manchester Division of Informatics Imaging and Data Sciences Manchester United Kingdom of Great Britain and Northern Ireland The University of Manchester School of Computer Science Manchester United Kingdom of Great Britain and Northern Ireland Manchester University NHS Foundation Trust Manchester United Kingdom of Great Britain and Northern Ireland Insitute of Artificial Intelligence in Medicine Essen University Hospital (AöR) Essen Germany Computer Algorithms for Medicine Laboratory Graz Austria Cancer Research Center Cologne Essen (CCCE) Essen Germany

The aortic vessel tree, composed of the aorta and its branches, is crucial for blood supply to the body. Aortic diseases, such as aneurysms and dissections, can lead to life-threatening ruptures, often requiring open surgery. Therefore, patients commonly undergo treatment under constant monitoring, which requires regular inspections of the vessels through medical imaging techniques. Overlapping and comparing aortic vessel tree geometries from consecutive images allows for tracking changes in both the aorta and its branches. Manual reconstruction of the vessel tree is time-consuming and impractical in clinical settings. In contrast, automatic or semi-automatic segmentation algorithms can perform this task much faster, making them suitable for routine clinical use. This paper systematically reviews methods for the automatic and semi-automatic segmentation of the aortic vessel tree, concluding with a discussion on their clinical applicability, the current research landscape, and ongoing challenges.

关键词： Aorta Vessel Tree Machine Learning Deep Learning Segmentation Analysis Cardiovascular Disease Aneurysm Dissection Computed Tomography.

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