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IEEE Conference on computer vision and Pattern Recognition

作者： Yixin Zhu Chenfanfu Jiang Yibiao Zhao Demetri Terzopoulos Song-Chun Zhu UCLA Center for Vision Cognition Learning and Autonomy UCLA Computer Graphics & Vision Laboratory

ISBN: (纸本)9781467388528

We propose a notion of affordance that takes into account physical quantities generated when the human body interacts with real-world objects, and introduce a learning framework that incorporates the concept of human utilities, which in our opinion provides a deeper and finer-grained account not only of object affordance but also of people's interaction with objects. Rather than defining affordance in terms of the geometric compatibility between body poses and 3D objects, we devise algorithms that employ physics-based simulation to infer the relevant forces/pressures acting on body parts. By observing the choices people make in videos (particularly in selecting a chair in which to sit) our system learns the comfort intervals of the forces exerted on body parts (while sitting). We account for people's preferences in terms of human utilities, which transcend comfort intervals to account also for meaningful tasks within scenes and spatiotemporal constraints in motion planning, such as for the purposes of robot task planning.

关键词： Three-dimensional displays Solid modeling Finite element analysis Planning Robots Biological system modeling Videos

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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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Coordinated Scheduling in MIMO Heterogeneous Wireless Networks using Submodular Optimization

Coordinated Scheduling in MIMO Heterogeneous Wireless Networ...

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International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks

作者： V. Singh R.J. La M.A. Shayman Computer Vision Laboratory IC Faculty École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Institute for Computer Graphics and Vision Graz University of Technology Graz Austria

ISBN: (纸本)9781509013128

Multiple transmission point (TP) coordination is an effective tool for managing interference in heterogeneous networks. Using network-level MIMO coordination among TPs, multiple TPs can transmit independent data streams to a single user. We formulate our scheduling problem as a utility optimization problem over the set of user-TP pairs in each subframe, in which each user can be served by multiple TPs. We first prove that this optimization problem is NP-hard. Then, we propose a new scheme based on the difference-of-submodular-function optimization approach. We evaluate our scheme using detailed simulation and demonstrate that it performs on par with a more computationally demanding successive convex approximation optimization scheme. Moreover, the proposed scheme performs within a reasonable percentage of the optimal solution (Table IV). We also study its performance with varying network topology and parameters.

关键词： Coordinated Multipoint scheduling difference of submodular function (DSF) optimization successive convex approximation

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Development of a computer-aided design software for dental splint in orthognathic surgery

arXiv

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arXiv 2017年

作者： Chen, Xiaojun Li, Xing Xu, Lu Sun, Yi Politis, Constantinus Egger, Jan Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China OMFS IMPATH Research Group Department of Imaging and Pathology Faculty of Medicine KU Leuven Leuven Belgium Department of Oral and Maxillofacial Surgery University Hospitals Leuven Leuven Belgium Faculty of Computer Science and Biomedical Engineering Institute for Computer Graphics and Vision Graz University of Technology Graz Austria BioTechMed-Graz Graz Austria

In the orthognathic surgery, dental splints are important and necessary to help the surgeon reposition the maxilla or mandible. However, the traditional methods of manual design of dental splints are difficult and time-consuming. The research on computer-aided design software for dental splints is rarely reported. Our purpose is to develop a novel special software named EasySplint to design the dental splints conveniently and efficiently. The design can be divided into two steps, which are the generation of initial splint base and the Boolean operation between it and the maxilla-mandibular model. The initial splint base is formed by ruled surfaces reconstructed using the manually picked points. Then, a method to accomplish Boolean operation based on the distance filed of two meshes is proposed. The interference elimination can be conducted on the basis of marching cubes algorithm and Boolean operation. The accuracy of the dental splint can be guaranteed since the original mesh is utilized to form the result surface. Using EasySplint, the dental splints can be designed in about 10 minutes and saved as a stereo lithography (STL) file for 3D printing in clinical applications. Three phantom experiments were conducted and the efficiency of our method was demonstrated. Copyright © 2017, The Authors. All rights reserved.

关键词： computer software

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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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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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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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Optimal surface normal from affine transformation 10

Optimal surface normal from affine transformation

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10th International Conference on computer vision Theory and Applications, VISAPP 2015

作者： Daniel, Barath Molnar, Jozsef Hajder, Levente Geometric Modelling and Computer Vision Laboratory MTA SZTAKI Kende utca 13-17 BudapestH-1111 Hungary Department of Image Processing and Computer Graphics Institute of Informatics University of Szeged Árpád tér 2 SzegedH-6720 Hungary

ISBN: (纸本)9789897580918

This paper deals with surface normal estimation from calibrated stereo images. We show here how the affine transformation between two projections defines the surface normal of a 3D planar patch. We give a formula that describes the relationship of surface normals, camera projections, and affine transformations. This formula is general since it works for every kind of cameras. We propose novel methods for estimating the normal of a surface patch if the affine transformation is known between two perspective images. We show here that the normal vector can be optimally estimated if the projective depth of the patch is known. Other non-optimal methods are also introduced for the problem. The proposed methods are tested both on synthesized data and images of real-world 3D objects.

关键词： Cameras

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A Novel Representation of Parts for Accurate 3D Object Detection and Tracking in Monocular Images

A Novel Representation of Parts for Accurate 3D Object Detec...

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International Conference on computer vision (ICCV)

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

ISBN: (纸本)9781467383929

We present a method that estimates in real-time and under challenging conditions the 3D pose of a known object. Our method relies only on grayscale images since depth cameras fail on metallic objects, it can handle poorly textured objects, and cluttered, changing environments, the pose it predicts degrades gracefully in presence of large occlusions. As a result, by contrast with the state-of-the-art, our method is suitable for practical Augmented Reality applications even in industrial environments. To be robust to occlusions, we first learn to detect some parts of the target object. Our key idea is to then predict the 3D pose of each part in the form of the 2D projections of a few control points. The advantages of this representation is three-fold: We can predict the 3D pose of the object even when only one part is visible, when several parts are visible, we can combine them easily to compute a better pose of the object, the 3D pose we obtain is usually very accurate, even when only few parts are visible.

关键词： Three-dimensional displays Cameras Robustness Training Object detection Augmented reality Image edge detection

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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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