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Learning to find good correspondences

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arXiv 2017年

作者： Yi, Kwang Moo Trulls, Eduard Ono, Yuki Lepetit, Vincent Salzmann, Mathieu Fua, Pascal Visual Computing Group University of Victoria Computer Vision Laboratory École Polytechnique Fédérale de Lausanne Sony Imaging Products and Solutions Inc. Institute for Computer Graphics and Vision Graz University of Technology

We develop a deep architecture to learn to find good cor-respondences for wide-baseline stereo. Given a set of pu-tative sparse matches and the camera intrinsics, we train our network in an end-to-end fashion to label the corre-spondences as inliers or outliers, while simultaneously us-ing them to recover the relative pose, as encoded by the essential matrix. Our architecture is based on a multi-layer perceptron operating on pixel coordinates rather than di-rectly on the image, and is thus simple and small. We intro-duce a novel normalization technique, called Context Nor-malization, which allows us to process each data point sep-arately while embedding global information in it, and also makes the network invariant to the order of the correspon-dences. Our experiments on multiple challenging datasets demonstrate that our method is able to drastically improve the state of the art with little training data. Copyright © 2017, The Authors. All rights reserved.

关键词： Network architecture

Evaluating physical quantities and learning human utilities from RGBD videos 16

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Evaluating physical quantities and learning human utilities ...

2016 SIGGRAPH ASIA Virtual Reality Meets Physical Reality: Modelling and Simulating Virtual Humans and Environments, SA 2016

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

ISBN: (纸本)9781450345484

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. © 2016 Copyright held by the owner/author(s).SA '16, December 05-08 2016.

关键词： Motion planning

Inferring forces and learning human utilities from videos

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Inferring forces and learning human utilities from videos

2016 IEEE Conference on computer vision and Pattern Recognition, CVPR 2016

ISBN: (纸本)9781467388511

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 physicsbased 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： Robot programming

Clinical evaluation of semi-automatic opensource algorithmic software segmentation of the mandibular bone: Practical feasibility and assessment of a new course of action

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arXiv 2018年

作者： Wallner, Jürgen Hochegger, Kerstin Chen, Xiaojun Mischak, Irene Reinbacher, Knut Pau, Mauro Zrnc, Tomislav Schwenzer-Zimmerer, Katja Zemann, Wolfgang Schmalstieg, Dieter Egger, Jan Department of Oral & Maxillofacial Surgery Medical University of Graz Auenbruggerplatz 5/1 Graz Austria Laboratory Graz Austria Institute for Computer Graphics and Vision Graz University of Technology Inffeldgasse 16c/II Graz Austria School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Department of Dental Medicine and Oral Health Medical University of Graz Billrothgasse 4 Graz Austria BioTechMed-Graz Krenngasse 37/1 Graz Austria

Introduction: computer assisted technologies based on algorithmic software segmentation are an increasing topic of interest in complex surgical cases. However-due to functional instability, time consuming software processes, personnel resources or licensed-based financial costs many segmentation processes are often outsourced from clinical centers to third parties and the industry. Therefore, the aim of this trial was to assess the practical feasibility of an easy available, functional stable and licensed-free segmentation approach to be used in the clinical practice. Material and methods: In this retrospective, randomized, controlled trail the accuracy and accordance of the opensource based segmentation algorithm GrowCut was assessed through the comparison to the manually generated ground truth of the same anatomy using 10 CT lower jaw data-sets from the clinical routine. Assessment parameters were the segmentation time, the volume, the voxel number, the Dice Score and the Hausdorff distance. Results: Overall semi-automatic GrowCut segmentation times were about one minute. Mean Dice Score values of over 85% and Hausdorff Distances below 33.5 voxel could be achieved between the algorithmic GrowCut-based segmentations and the manual generated ground truth schemes. Statistical differences between the assessment parameters were not significant (p0.94) for any of the comparison made between the two groups. Discussion: Complete functional stable and time saving segmentations with high accuracy and high positive correlation could be performed by the presented interactive open-source based approach. In the cranio-maxillofacial complex the used method could represent an algorithmic alternative for image-based segmentation in the clinical practice for e.g. surgical treatment planning or visualization of postoperative results and offers several advantages. Due to an open-source basis the used method could be further developed by other groups or specialists. Systematic compari

关键词： Geometry

Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge

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arXiv 2019年

作者： Zhuang, Xiahai Li, Lei Payer, Christian Štern, Darko Urschler, Martin Heinrich, Mattias P. Oster, Julien Wang, Chunliang Smedby, Örjan Bian, Cheng Yang, Xin Heng, Pheng-Ann Mortazi, Aliasghar Bagci, Ulas Yang, Guanyu Sun, Chenchen Galisot, Gaetan Ramel, Jean-Yves Brouard, Thierry Tong, Qianqian Si, Weixin Liao, Xiangyun Zeng, Guodong Shi, Zenglin Zheng, Guoyan Wang, Chengjia MacGillivray, Tom Newby, David Rhode, Kawal Ourselin, Sebastien Mohiaddin, Raad Keegan, Jennifer Firmin, David Yang, Guang School of Data Science Fudan University Shanghai200433 China Fudan-Xinzailing Joint Research Center for Big Data Fudan University Shanghai200433 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Ludwig Boltzmann Institute for Clinical Forensic Imaging Graz8010 Austria Institute of Medical Informatics University of Lubeck Lubeck23562 Germany Inserm Université de Lorraine U1254 IADI Nancy France School for Technology and Health KTH Royal Institute of Technology StockholmSE-10044 Sweden School of Biomed. Eng. Health Science Centre Shenzhen University Shenzhen518060 China Dept. of Comp. Sci. and Eng. Chinese University of Hong Kong Hong Kong China University of Central Florida Orlando32816 United States School of Computer Science and Engineering Southeast University Nanjing210096 China Université de Tours 64 avenue Jean Portalis Tours37200 France School of Computer Science Wuhan University Wuhan430072 China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology SIAT Shenzhen China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Institute for Surgical Technology & Biomechanics University of Bern Bern3014 Switzerland BHF Centre for Cardiovascular Science University of Edinburgh Edinburgh United Kingdom Edinburgh Imaging Facility QMRI University of Edinburgh Edinburgh United Kingdom Department of Imaging Sciences & Biomedical Engineering Kings College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital LondonSW3 6NP United Kingdom National Heart and Lung Institute Imperial College London London LondonSW7 2AZ United Kingdom

Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be arduous due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, a set of training data is generally needed for constructing priors or for training. In addition, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provides 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results show that many of the deep learning (DL) based methods achieved high accuracy, even though the number of training datasets were limited. A number of them also reported poor results in the blinded evaluation, probably due to overfitting in their training. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated robust and stable performance, even though the accuracy is not as good as the best DL method in CT segmentation. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (***/zhuangxiahai/0/mmwhs/). Copyright © 2019, The Authors. All rights reserved.

关键词： Heart

Deep learning in remote sensing: a review

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arXiv 2017年

作者： Zhu, Xiao Xiang Tuia, Devis Mou, Lichao Xia, Gui-Song Zhang, Liangpei Xu, Feng Fraundorfer, Friedrich Germany Germany Department of Geography University of Zurich Switzerland Laboratory of GeoInformation Science and Remote Sensing Wageningen University of Research Netherlands Wuhan University Fudan Univeristy Institute of Computer Graphics and Vision TU Graz Germany

This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Magazine on IEEE XPlore. Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a "black-box" solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization. Copyright © 2017, The Authors. All rights reserved.

关键词： Remote sensing

A virtual reality platform for dynamic human-scene interaction 16

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A virtual reality platform for dynamic human-scene interacti...

2016 SIGGRAPH ASIA Virtual Reality Meets Physical Reality: Modelling and Simulating Virtual Humans and Environments, SA 2016

作者： Lin, Jenny Guo, Xingwen Shao, Jingyu Jiang, Chenfanfu Zhu, Yixin Zhu, Song-Chun UCLA Center for Vision Cognition Learning and Autonomy United States University of Hong Kong Electrical and Electronic Engineering Department Hong Kong UCLA Computer Graphics and Vision Laboratory United States

ISBN: (纸本)9781450345484

Both synthetic static and simulated dynamic 3D scene data is highly useful in the fields of computer vision and robot task planning. Yet their virtual nature makes it difficult for real agents to interact with such data in an intuitive way. Thus currently available datasets are either static or greatly simplified in terms of interactions and dynamics. In this paper, we propose a system in which Virtual Reality and human/finger pose tracking is integrated to allow agents to interact with virtual environments in real time. Segmented object and scene data is used to construct a scene within Unreal Engine 4, a physics-based game engine. We then use the Oculus Rift headset with a Kinect sensor, Leap Motion controller and a dance pad to navigate and manipulate objects inside synthetic scenes in real time. We demonstrate how our system can be used to construct a multi-jointed agent representation as well as fine-grained finger pose. In the end, we propose how our system can be used for robot task planning and image semantic segmentation. © 2016 Copyright held by the owner/author(s). SA '16, December 05-08 2016.

关键词： Virtual reality

computer-aided implant design for the restoration of cranial defects

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arXiv 2017年

作者： Chen, Xiaojun Xu, Lu Li, Xing 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 Faculty of Computer Science and Biomedical Engineering Institute for Computer Graphics and Vision Graz University of Technology Graz Austria BioTechMed-Graz Graz Austria

Patient-specific cranial implants are important and necessary in the surgery of cranial defect restoration. However, traditional methods of manual design of cranial implants are complicated and time-consuming. Our purpose is to develop a novel software named EasyCrania to design the cranial implants conveniently and efficiently. The process can be divided into five steps, which are mirroring model, clipping surface, surface fitting, the generation of the initial implant and the generation of the final implant. The main concept of our method is to use the geometry information of the mirrored model as the base to generate the final implant. The comparative studies demonstrated that the EasyCrania can improve the efficiency of cranial implant design significantly. And, the intra- and inter-rater reliability of the software were stable, which were 87.07 ± 1.6% and 87.73 ± 1.4% respectively. Copyright © 2017, The Authors. All rights reserved.

关键词： Restoration

A feasibility study on SSVEP-based interaction with motivating and immersive virtual and augmented reality

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arXiv 2017年

作者： Faller, J. Allison, B.Z. Brunner, C. Scherer, R. Schmalstieg, D. Pfurtscheller, G. Neuper, C. Institute for Knowledge Discovery Laboratory of Brain-Computer Interfaces Graz University of Technology Krenngasse 37 Graz8010 Austria Institute for Computer Graphics and Vision Graz University of Technology Inffeldgasse 16c Graz8010 Austria Department of Psychology University of Graz Universitätsplatz 2/III Graz8010 Austria

Non-invasive steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) systems offer high bandwidth compared to other BCI types and require only minimal calibration and training. Virtual reality (VR) has been already validated as effective, safe, affordable and motivating feedback modality for BCI experiments. Augmented reality (AR) enhances the physical world by superimposing informative, context sensitive, computer generated content. In the context of BCI, AR can be used as a friendlier and more intuitive real-world user interface, thereby facilitating a more seamless and goal directed interaction. This can improve practicality and usability of BCI systems and may help to compensate for their low bandwidth. In this feasibility study, three healthy participants had to finish a complex navigation task in immersive VR and AR conditions using an online SSVEP BCI. Two out of three subjects were successful in all conditions. To our knowledge, this is the first work to present an SSVEP BCI that operates using target stimuli integrated in immersive VR and AR (head-mounted display and camera). This research direction can benefit patients by introducing more intuitive and effective real-world interaction (e.g. smart home control). It may also be relevant for user groups that require or benefit from hands free operation (e.g. due to temporary situational disability). Copyright © 2017, The Authors. All rights reserved.

关键词： Interface states