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检索条件"机构=Computer Vision and Graphics Laboratory"

共 153 条记录，以下是41-50 订阅

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Planar roof surface segmentation using 3D vision 11

Planar roof surface segmentation using 3D vision

引用

19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2011

作者： Meixner, Philipp Leberl, Franz Brédif, Mathieu Institute of Computer Graphics and Vision Graz University of Technology A-8010 Graz Austria MATIS Laboratory Institute Géographique Nationale Université Paris-Est 94165 Saint-Mandé Cedex France

ISBN: (纸本)9781450310314

Internet search has initially been a strong driving force for the rapid emergence of 3D building models of large urban areas. Additionally, many commercial and governmental initiatives have been started to develop urban 3D geographic information systems in a transition from the classical 2D- to the novel 3D-GIS. The modeling of building roofs is thus a relevant research topic. The focus has been on the use of aerial LiDAR point clouds (Light Detection And Ranging). However, recent progress in digital aerial cameras has rendered possible the acquisition of very dense point clouds from high overlap digital aerial imagery, and to use these point clouds jointly with the image information to generate 3D building models. This paper presents a multi-step processing framework and work flow for the automatic segmentation of building roofs in densely built-up areas from high-resolution vertical aerial images. Details extruding from, or intruding into, a roof are being excluded so that each roof is being modeled by means of its planar segments and can then be classified as a specific roof type from a set of standard roof shapes. Our experimental work employs a test area in Graz (Austria) with 186 buildings. © 2011 ACM.

关键词： Roofs

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Improving Classifiers with Unlabeled Weakly-Related Videos

Improving Classifiers with Unlabeled Weakly-Related Videos

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IEEE Conference on computer vision and Pattern Recognition

作者： Christian Leistner Martin Godec Samuel Schulter Amir Saffari Manuel Werlberger Horst Bischof Computer Vision Laboratory ETH Zurich Institute for Computer Graphics and Vision TU Graz Oxford Brookes University and Sony Computer Entertainment

ISBN: (纸本)9781457703942

Current state-of-the-art object classification systems are trained using large amounts of hand-labeled images. In this paper, we present an approach that shows how to use unlabeled video sequences, comprising weakly-related object categories towards the target class, to learn better classifiers for tracking and detection. The underlying idea is to exploit the space-time consistency of moving objects to learn classifiers that are robust to local transformations. In particular, we use dense optical flow to find moving objects in videos in order to train part-based random forests that are insensitive to natural transformations. Our method, which is called Video Forests, can be used in two settings: first, labeled training data can be regularized to force the trained classifier to generalize better towards small local transformations. Second, as part of a tracking-by-detection approach, it can be used to train a general codebook solely on pair-wise data that can then be applied to tracking of instances of a priori unknown object categories. In the experimental part, we show on benchmark datasets for both tracking and detection that incorporating unlabeled videos into the learning of visual classifiers leads to improved results.

关键词： Videos Target tracking Vegetation Training Optical imaging computer vision

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Learning to Assign Orientations to Feature Points

Learning to Assign Orientations to Feature Points

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IEEE Conference on computer vision and Pattern Recognition

作者： Kwang Moo Yi Yannick Verdie Pascal Fua Vincent Lepetit Computer Vision Laboratory Ecole Polytechnique Federale de Lausanne (EPFL) Institute for Computer Graphics and Vision Graz University of Technology

ISBN: (纸本)9781467388528

We show how to train a Convolutional Neural Network to assign a canonical orientation to feature points given an image patch centered on the feature point. Our method improves feature point matching upon the state-of-the art and can be used in conjunction with any existing rotation sensitive descriptors. To avoid the tedious and almost impossible task of finding a target orientation to learn, we propose to use Siamese networks which implicitly find the optimal orientations during training. We also propose a new type of activation function for Neural Networks that generalizes the popular ReLU, maxout, and PReLU activation functions. This novel activation performs better for our task. We validate the effectiveness of our method extensively with four existing datasets, including two non-planar datasets, as well as our own dataset. We show that we outperform the state-of-the-art without the need of retraining for each dataset.

关键词： Feature Points image patch Dataset Arts Neural network Activating function Vocational Rehabilitation Orientations Orientation Learning

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[DEMO] Tracking texture-less, shiny objects with descriptor fields

[DEMO] Tracking texture-less, shiny objects with descriptor ...

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International Symposium on Mixed and Augmented Reality (ISMAR)

作者： Alberto Crivellaro Yannick Verdie Kwang Moo Yi Pascal Fua Vincent Lepetit Computer Vision Laboratory École Polytechnique Federale de Lausanne (EPFL) Institute for Computer Graphics and Vision Graz University of Technology

Our demo demonstrates the method we published at CVPR this year for tracking specular and poorly textured objects, and lets the visitors experiment with it and with their own patterns. Our approach only requires a standard monocular camera (no need for a depth sensor), and can be easily integrated within existing systems to improve their robustness and accuracy.

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Flying Objects Detection from a Single Moving Camera

Flying Objects Detection from a Single Moving Camera

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IEEE Conference on computer vision and Pattern Recognition

作者： Artem Rozantsev Vincent Lepetit Pascal Fua Computer Vision Laboratory Ecole Polytechnique Federale de Lausanne (EPFL) Institute for Computer Graphics and Vision Graz University of Technology

ISBN: (纸本)9781467369657

We propose an approach to detect flying objects such as UAVs and aircrafts when they occupy a small portion of the field of view, possibly moving against complex backgrounds, and are filmed by a camera that itself moves. Solving such a difficult problem requires combining both appearance and motion cues. To this end we propose a regression-based approach to motion stabilization of local image patches that allows us to achieve effective classification on spatio-temporal image cubes and outperform state-of-the-art techniques. As the problem is relatively new, we collected two challenging datasets for UAVs and Aircrafts, which can be used as benchmarks for flying objects detection and vision-guided collision avoidance.

关键词： Aircraft Cameras Flying Unmanned Aerial Vehicle Collision avoidance Moving field of view BENCHMARKS image patch

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Multiscale Centerline Detection by Learning a Scale-Space Distance Transform

Multiscale Centerline Detection by Learning a Scale-Space Di...

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IEEE Conference on computer vision and Pattern Recognition

作者： Amos Sironi Vincent Lepetit Pascal Fua Computer Vision Laboratory Ecole Polytechnique Federale de Lausanne (EPFL) Institute for Computer Graphics and Vision Graz University of Technology

ISBN: (纸本)9781479951192

We propose a robust and accurate method to extract the centerlines and scale of tubular structures in 2D images and 3D volumes. Existing techniques rely either on filters designed to respond to ideal cylindrical structures, which lose accuracy when the linear structures become very irregular, or on classification, which is inaccurate because locations on centerlines and locations immediately next to them are extremely difficult to distinguish. We solve this problem by reformulating centerline detection in terms of a regression problem. We first train regressors to return the distances to the closest centerline in scale-space, and we apply them to the input images or volumes. The centerlines and the corresponding scale then correspond to the regressors local maxima, which can be easily identified. We show that our method outperforms state-of-the-art techniques for various 2D and 3D datasets.

关键词： Three-dimensional displays Training Transforms Image segmentation Vectors Feature extraction Robustness

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Estimation Illumination Chromaticity

Estimation Illumination Chromaticity

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International Symposium on Intelligent Information Technology Application, IITA

作者： Li Yao Computer Graphics & Vision Laboratory South-East University Nanjing Jiangsu China

Color constancy is important for pattern recognition and image understanding. Based on simplified BRDF model, we use voting method to detect the high light areas. Then the pixels in the high light areas are projected to the inverse-intensity space. In the inverse-intensity space, the correlation of image chromaticity, inverse-intensity, and illumination chromaticity is linear. So we can estimate the Illumination chromaticity. The experiments demonstrate that our algorithm have no limit on the objects' material and texture and contain the color constancy after the illumination chromaticity correction.

关键词： Lighting Optical reflection Cameras Layout Color Surface texture Information technology Application software computer graphics Pattern recognition

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TILDE: A Temporally Invariant Learned DEtector

TILDE: A Temporally Invariant Learned DEtector

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IEEE Conference on computer vision and Pattern Recognition

作者： Yannick Verdie Kwang Moo Yi Pascal Fua Vincent Lepetit Computer Vision Laboratory Ecole Polytechnique Federale de Lausanne (EPFL) Institute for Computer Graphics and Vision Graz University of Technology

ISBN: (纸本)9781467369657

We introduce a learning-based approach to detect repeatable keypoints under drastic imaging changes of weather and lighting conditions to which state-of-the-art keypoint detectors are surprisingly sensitive. We first identify good keypoint candidates in multiple training images taken from the same viewpoint. We then train a regressor to predict a score map whose maxima are those points so that they can be found by simple non-maximum suppression. As there are no standard datasets to test the influence of these kinds of changes, we created our own, which we will make publicly available. We will show that our method significantly outperforms the state-of-the-art methods in such challenging conditions, while still achieving state-of-the-art performance on untrained standard datasets.

关键词： detector Dataset Invariant Weather Lighting Disease Predictive Factor TRAIN

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Semi-supervised Virtual Regression of Aortic Dissections Using 3D Generative Inpainting 2nd

Semi-supervised Virtual Regression of Aortic Dissections Usi...

引用

2nd International Workshop on Thoracic Image Analysis, TIA 2020 Held in Conjunction with Medical Image Computing and computer-Assisted Intervention Conference, MICCAI 2020

作者： Pepe, Antonio Mistelbauer, Gabriel Gsaxner, Christina Li, Jianning Fleischmann, Dominik Schmalstieg, Dieter Egger, Jan Institute of Computer Graphics and Vision Graz University of Technology Graz Austria School of Medicine Department of Radiology Stanford University StanfordCA United States Computer Algorithms in Medicine Laboratory Graz Austria Department of Simulation and Graphics University of Magdeburg Magdeburg Germany Department of Oral and Maxillofacial Surgery Medical University of Graz Graz Austria

ISBN: (纸本)9783030624682

Aortic dissection (AD) is a condition of the main artery of the human body, resulting in the formation of a new flow channel, or false lumen (FL). The disease is usually diagnosed with a computed tomography angiography (CTA) scan during the acute phase. A better understanding of the causes of AD requires knowledge of aortic geometry prior to the event, which is available only in very rare circumstances. In this work, we propose an approach to reconstruct the aorta before the formation of a dissection by performing 3D inpainting with a two-stage generative adversarial network (GAN). In the first stage of our two-stage GAN, a network is trained on the 3D edge information of the healthy aorta to reconstruct the aortic wall. The second stage infers the image information of the aorta to reconstruct the entire dataset. We train our two-stage GAN with 3D patches from 55 non-dissected aortic datasets and evaluate it on 20 more non-dissected datasets, demonstrating that our proposed 3D architecture outperforms its 2D counterpart. To obtain pre-dissection aortae, we mask the entire FL in AD datasets. Finally, we provide qualitative feedback from a renown expert on the obtained pre-dissection cases. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Fast anatomical structure localization using top-down image patch regression

Fast anatomical structure localization using top-down image ...

引用

15th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2012

作者： Donner, René Menze, Bjoern H. Bischof, Horst Langs, Georg Computational Image Analysis and Radiology Lab. Department of Radiology Medical University Vienna Austria Institute for Computer Graphics and Vision Graz University of Technology Austria CSAIL MIT Cambridge MA United States Asclepios Project INRIA Sophia-Antipolis France Computer Vision Laboratory ETH Zurich Switzerland

ISBN: (纸本)9783642366192

Fully automatic localization of anatomical structures in 2D and 3D radiological data sets is important in both computer aided diagnosis, and the rapid automatic processing of large amounts of data. We present a simple, accurate and fast approach with low computational complexity to find anatomical landmarks, based on a multi-scale regression codebook of informative image patches and encoded landmark contexts. From a set of annotated training volumes the method captures the appearance of landmarks over several scales together with relative positions of neighboring landmarks and a spatial distribution model. During multi-scale search in a target volume, starting from the coarsest level, each landmark model predicts all landmark positions it has encoded, with the median of all predictions yielding the final prediction for each scale. We present results on two challenging data sets (hand radiographs and hand CTs), where our method achieves comparable accuracy to the state of the art with substantially improved run-time. © 2013 Springer-Verlag.

关键词： computer aided diagnosis

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