检索结果-内蒙古大学图书馆

arXiv 2017年

作者： Park, Jinsun Tai, Yu-Wing Cho, Donghyeon Kweon, In So Robotics and Computer Vision Lab. KAIST Tencent YouTu Lab.

In this paper, we introduce robust and synergetic handcrafted features and a simple but efficient deep feature from a convolutional neural network (CNN) architecture for defocus estimation. This paper systematically analyzes the effectiveness of different features, and shows how each feature can compensate for the weaknesses of other features when they are concatenated. For a full defocus map estimation, we extract image patches on strong edges sparsely, after which we use them for deep and hand-crafted feature extraction. In order to reduce the degree of patch-scale dependency, we also propose a multi-scale patch extraction strategy. A sparse defocus map is generated using a neural network classifier followed by a probability-joint bilateral filter. The final defocus map is obtained from the sparse defocus map with guidance from an edge-preserving filtered input image. Experimental results show that our algorithm is superior to state-of-the-art algorithms in terms of defocus estimation. Our work can be used for applications such as segmentation, blur magnification, all-in-focus image generation, and 3-D estimation. Copyright © 2017, The Authors. All rights reserved.

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

Globally optimal manhattan frame estimation in real-time

Globally optimal manhattan frame estimation in real-time

引用

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

作者： Joo, Kyungdon Oh, Tae-Hyun Kim, Junsik Kweon, In So Robotics and Computer Vision Lab. KAIST Korea Republic of

ISBN: (纸本)9781467388511

Given a set of surface normals, we pose a Manhattan Frame (MF) estimation problem as a consensus set maximization that maximizes the number of inliers over the rotation search space. We solve this problem through a branchand-bound framework, which mathematically guarantees a globally optimal solution. However, the computational time of conventional branch-and-bound algorithms are intractable for real-time performance. In this paper, we propose a novel bound computation method within an efficient measurement domain for MF estimation, i.e., the extended Gaussian image (EGI). By relaxing the original problem, we can compute the bounds in real-time, while preserving global optimality. Furthermore, we quantitatively and qualitatively demonstrate the performance of the proposed method for synthetic and real-world data. We also show the versatility of our approach through two applications: extension to multiple MF estimation and video stabilization.

关键词： Problem solving

来源：评论

学校读者我要写书评

暂无评论

VPGNet: Vanishing point guided network for lane and road marking detection and recognition

arXiv

引用

arXiv 2017年

作者： Lee, Seokju Kim, Junsik Yoon, Jae Shin Shin, Seunghak Bailo, Oleksandr Kim, Namil Lee, Tae-Hee Hong, Hyun Seok Han, Seung-Hoon Kweon, In So Robotics and Computer Vision Lab KAIST Samsung Electronics DMC R&D Center

In this paper, we propose a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point under adverse weather conditions. We tackle rainy and low illumination conditions, which have not been extensively studied until now due to clear challenges. For example, images taken under rainy days are subject to low illumination, while wet roads cause light reflection and distort the appearance of lane and road markings. At night, color distortion occurs under limited illumination. As a result, no benchmark dataset exists and only a few developed algorithms work under poor weather conditions. To address this shortcoming, we build up a lane and road marking benchmark which consists of about 20,000 images with 17 lane and road marking classes under four different scenarios: no rain, rain, heavy rain, and night. We train and evaluate several versions of the proposed multi-task network and validate the importance of each task. The resulting approach, VPGNet, can detect and classify lanes and road markings, and predict a vanishing point with a single forward pass. Experimental results show that our approach achieves high accuracy and robustness under various conditions in real-time (20 fps). The benchmark and the VPGNet model will be publicly availab.e 1. Copyright © 2017, The Authors. All rights reserved.

关键词： Road and street markings

来源：评论

学校读者我要写书评

暂无评论

Stereo matching with color and monochrome cameras in low-light conditions

Stereo matching with color and monochrome cameras in low-lig...

引用

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

作者： Jeon, Hae-Gon Lee, Joon-Young Im, Sunghoon Ha, Hyowon Kweon, In So Robotics and Computer Vision Lab. KAIST Korea Republic of Adobe Research United States

ISBN: (纸本)9781467388511

Consumer devices with stereo cameras have become popular because of their low-cost depth sensing capability. However, those systems usually suffer from low imaging quality and inaccurate depth acquisition under low-light conditions. To address the problem, we present a new stereo matching method with a color and monochrome camera pair. We focus on the fundamental trade-off that monochrome cameras have much better light-efficiency than color-filtered cameras. Our key ideas involve compensating for the radiometric difference between two crossspectral images and taking full advantage of complementary data. Consequently, our method produces both an accurate depth map and high-quality images, which are applicable for various depth-aware image processing. Our method is evaluated using various datasets and the performance of our depth estimation consistently outperforms state-of-the-art methods.

关键词： Stereo image processing

来源：评论

学校读者我要写书评

暂无评论

Why is the Winner the Best?

Why is the Winner the Best?

引用

Conference on computer vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Efficient 3D hand tracking in articulation subspaces for the manipulation of virtual objects 16

Efficient 3D hand tracking in articulation subspaces for the...

引用

33rd computer Graphics International Conference, CGI 2016

作者： Park, Gabyong Argyros, Antonis Woo, Woontack KAIST GSCT UVR Lab. Daejeon Korea Republic of Computational Vision and Robotics Lab. Institute of Computer Science FORTH University of Crete Greece

ISBN: (纸本)9781450341233

We propose an efficient method for model-based 3D tracking of hand articulations observed from an egocentric viewpoint that aims at supporting the manipulation of virtual objects. Previous modelbased approaches optimize non-convex objective functions defined in the 26 Degrees of Freedom (DoFs) space of pobible hand articulations. In our work, we decompose this space into six articulation subspaces (6 DoFs for the palm and 4 DoFs for each finger). We also lab.l each finger with a Gaubian model that is propagated between succebive image frames. As confirmed by a number of experiments, this divide-and-conquer approach tracks hand articulations more accurately than existing model-based approaches. At the same time, real time performance is achieved without the need of GPGPU procebing. Additional experiments show that the proposed approach is preferable for supporting the accurate manipulation of virtual objects in VR/AR scenarios. © 2016 ACM.

关键词： Degrees of freedom (mechanics)

来源：评论

学校读者我要写书评

暂无评论

Integrating mission, logistics, and task planning for skills-based robot control in industrial kitting applications 34

Integrating mission, logistics, and task planning for skills...

引用

34th Workshop of the UK Planning and Scheduling Special Interest Group, PlanSIG 2016

作者： Crosby, Matthew Petrick, Ronald P. A. Toscano, César Dias, Rui Correia Rovida, Francesco Krüger, Volker Department of Computer Science Heriot-Watt University EdinburghEH14 4AS United Kingdom INESC TEC Technology and Science Porto4200 - 465 Portugal Robotics Vision and Machine Intelligence Lab. Aalborg University Copenhagen2450 Denmark

This paper presents an integrated cognitive robotics system for industrial kitting operations in a modern factory setting. The robot system combines low-level robot control and execution monitoring with automated mission and task planning, and a logistics planner which communicates with the factory's manufacturing execution system. The system has been implemented and tested on a series of automotive kitting problems, where collections of parts are picked from a warehouse and delivered to the production line. The system has been empirically evaluated and the complete framework shown to be successful at assembling kits in a small factory environment. © 2017, CEUR-WS. All rights reserved.

关键词： Robot programming

来源：评论

学校读者我要写书评

暂无评论

Globally Optimal Manhattan Frame Estimation in Real-time

Globally Optimal Manhattan Frame Estimation in Real-time

引用

IEEE Conference on computer vision and Pattern Recognition

作者： Kyungdon Joo Tae-Hyun Oh Junsik Kim In So Kweon Robotics and Computer Vision Lab. KAIST

ISBN: (纸本)9781467388528

Given a set of surface normals, we pose a Manhattan Frame (MF) estimation problem as a consensus set maximization that maximizes the number of inliers over the rotation search space. We solve this problem through a branch-and-bound framework, which mathematically guarantees a globally optimal solution. However, the computational time of conventional branch-and-bound algorithms are intractable for real-time performance. In this paper, we propose a novel bound computation method within an efficient measurement domain for MF estimation, i.e., the extended Gaussian image (EGI). By relaxing the original problem, we can compute the bounds in real-time, while preserving global optimality. Furthermore, we quantitatively and qualitatively demonstrate the performance of the proposed method for synthetic and real-world data. We also show the versatility of our approach through two applications: extension to multiple MF estimation and video stabilization.

关键词： Manhattan surface normal inliers Real time computational methods Frame EGI gene SYNTHETICS

来源：评论

学校读者我要写书评

暂无评论

Stereo Matching with Color and Monochrome Cameras in Low-light Conditions

Stereo Matching with Color and Monochrome Cameras in Low-lig...

引用

IEEE Conference on computer vision and Pattern Recognition

作者： Hae-Gon Jeon Joon-Young Lee Sunghoon Im Hyowon Ha In So Kweon Robotics and Computer Vision Lab. KAIST Adobe Research

ISBN: (纸本)9781467388528

Consumer devices with stereo cameras have become popular because of their low-cost depth sensing capability. However, those systems usually suffer from low imaging quality and inaccurate depth acquisition under low-light conditions. To address the problem, we present a new stereo matching method with a color and monochrome camera pair. We focus on the fundamental trade-off that monochrome cameras have much better light-efficiency than color-filtered cameras. Our key ideas involve compensating for the radiometric difference between two cross-spectral images and taking full advantage of complementary data. Consequently, our method produces both an accurate depth map and high-quality images, which are applicable for various depth-aware image processing. Our method is evaluated using various datasets and the performance of our depth estimation consistently outperforms state-of-the-art methods.

关键词： Cameras Image quality Stereo cameras Color Dataset Image processing

来源：评论

学校读者我要写书评

暂无评论

Accurate Camera Calibration Robust to Defocus using a Smartphone

Accurate Camera Calibration Robust to Defocus using a Smartp...

引用

IEEE International Conference on computer vision

作者： Hyowon Ha Yunsu Bok Kyungdon Joo Jiyoung Jung In So Kweon Robotics and Computer Vision Lab. KAIST

ISBN: (纸本)9781467383929

We propose a novel camera calibration method for defocused images using a smartphone under the assumption that the defocus blur is modeled as a convolution of a sharp image with a Gaussian point spread function (PSF). In contrast to existing calibration approaches which require well-focused images, the proposed method achieves accurate camera calibration with severely defocused images. This robustness to defocus is due to the proposed set of unidirectional binary patterns, which simplifies 2D Gaussian deconvolution to a 1D Gaussian deconvolution problem with multiple observations. By capturing the set of patterns consecutively displayed on a smartphone, we formulate the feature extraction as a deconvolution problem to estimate feature point locations in sub-pixel accuracy and the blur kernel in each location. We also compensate the error in camera parameters due to refraction of the glass panel of the display device. We evaluate the performance of the proposed method on synthetic and real data. Even under severe defocus, our method shows accurate camera calibration result.

关键词： defocusing camera calibration Smartphone GLASS PLATES display devices Feature extraction deconvolution

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：