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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2015"

共 4931 条记录，以下是91-100 订阅

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Stacked U-Nets with Multi-Output for Road Extraction 31

Stacked U-Nets with Multi-Output for Road Extraction

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Sun, Tao Chen, Zehui Yang, Wenxiang Wang, Yin Tongji Univ Shanghai Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

With the recent advances of Convolutional Neural Networks (CNN) in computer vision, there have been rapid progresses in extracting roads and other features from satellite imagery for mapping and other purposes. In this paper, we propose a new method for road extraction using stacked U-Nets with multiple output. A hybrid loss function is used to address the problem of unbalanced classes of training data. Post-processing methods, including road map vectorization and shortest path search with hierarchical thresholds, help improve recall. The overall improvement of mean IoU compared to the vanilla VGG network is more than 20%.

关键词： Roads Decoding computer vision Encoding Feature extraction Neural networks Satellites

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Shadow Removal with Paired and Unpaired Learning

Shadow Removal with Paired and Unpaired Learning

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Vasluianu, Florin-Alexandru Romero, Andres Van Gool, Luc Timofte, Radu Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781665448994

Shadow removal is an important computer vision task aiming at the detection and successful removal of the shadow produced by an occluded light source and a photorealistic restoration of the image contents. Decades of research produced a multitude of hand-crafted restoration techniques and, more recently, learned solutions from shadowed and shadow free training image pairs. In this work, we propose a single image shadow removal solution via self-supervised learning by using a conditioned mask. We rely on self-supervision and jointly learn deep models to remove and add shadows to images. We derive two variants for learning from paired images and unpaired images, respectively. Our validation on the recently introduced ISTD and USR datasets demonstrate large quantitative and qualitative improvements over the state-of-the-art for both paired and unpaired learning settings.

关键词： Training computer vision conferences Image restoration pattern recognition Task analysis Light sources

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Anomaly Detection in Autonomous Driving: A Survey

Anomaly Detection in Autonomous Driving: A Survey

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Bogdoll, Daniel Nitsche, Maximilian Zoellner, J. Marius FZI Res Ctr Informat Technol Karlsruhe Germany KIT Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9781665487399

Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set conditions, they still struggle to handle the unexpected. This survey provides an extensive overview of anomaly detection techniques based on camera, lidar, radar, multimodal and abstract object level data. We provide a systematization including detection approach, corner case level, ability for an online application, and further attributes. We outline the state-of-the-art and point out current research gaps.

关键词： computer vision Laser radar Roads conferences Radar detection Benchmark testing Cameras

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Pixel-Guided Dual-Branch Attention Network for Joint Image Deblurring and Super-Resolution

Pixel-Guided Dual-Branch Attention Network for Joint Image D...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Xi, Si Wei, Jia Zhang, Weidong Netease Games AI Lab Hangzhou Peoples R China

ISBN: (纸本)9781665448994

Image deblurring and super-resolution (SR) are computer vision tasks aiming to restore image detail and spatial scale, respectively. Besides, only a few recent works of literature contribute to this task, as conventional methods deal with SR or deblurring separately. We focus on designing a novel Pixel-Guided dual-branch attention network (PDAN) that handles both tasks jointly to address this issue. Then, we propose a novel loss function better focus on large and medium range errors. Extensive experiments demonstrated that the proposed PDAN with the novel loss function not only generates remarkably clear HR images and achieves compelling results for joint image deblurring and SR tasks. In addition, our method achieves second place in NTIRE 2021 Challenge on track 1 of the Image Deblurring Challenge.

关键词： Training computer vision conferences Superresolution Feature extraction Image restoration pattern recognition

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Line Art Colorization with Concatenated Spatial Attention

Line Art Colorization with Concatenated Spatial Attention

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yuan, Mingcheng Simo-Serra, Edgar Waseda Univ Tokyo Japan

ISBN: (纸本)9781665448994

Line art plays a fundamental role in illustration and design, and allows for iteratively polishing designs. However, as they lack color, they can have issues in conveying final designs. In this work, we propose an interactive colorization approach based on a conditional generative adversarial network that takes both the line art and color hints as inputs to produce a high-quality colorized image. Our approach is based on a U-net architecture with a multi-discriminator framework. We propose a Concatenation and Spatial Attention module that is able to generate more consistent and higher quality of line art colorization from user given hints. We evaluate on a large-scale illustration dataset and comparison with existing approaches corroborate the effectiveness of our approach.

关键词： Adaptation models computer vision Art Image resolution Image color analysis Computational modeling conferences

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3D Ground-Truth Systems for Object/Human recognition and Tracking

3D Ground-Truth Systems for Object/Human Recognition and Tra...

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26th ieee conference on computer vision and pattern recognition (CVPR)

作者： Godil, Afzal Bostelman, Roger Saidi, Kamel Shackleford, Will Cheok, Geraldine Shneier, Michael Hong, Tsai NIST Gaithersburg MD 20899 USA

ISBN: (纸本)9780769549903

We have been researching three dimensional (3D) ground-truth systems for performance evaluation of vision and perception systems in the fields of smart manufacturing and robot safety. In this paper we first present an overview of different systems that have been used to provide ground-truth (GT) measurements and then we discuss the advantages of physically-sensed ground-truth systems for our applications. Then we discuss in detail the three ground- truth systems that we have used in our experiments: ultra wide-band, indoor GPS, and a camera-based motion capture system. Finally, we discuss three different perception-evaluation experiments where we have used these GT systems

关键词： computer vision image motion analysis object recognition object tracking visual perception

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As Seen on TV: Automatic Basketball Video Production using Gaussian-based Actionness and Game States recognition

As Seen on TV: Automatic Basketball Video Production using G...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Quiroga, Julian Carrillo, Henry Maldonado, Edisson Ruiz, John Zapata, Luis M. Genius Sports Comp Vis Medellin Colombia

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Automatic video production of sports aims at producing an aesthetic broadcast of sporting events. We present a new video system able to automatically produce a smooth and pleasant broadcast of Basketball games using a single fixed 4K camera. The system automatically detects and localizes players, ball and referees, to recognize main action coordinates and game states yielding to a professional cameraman-like production of the basketball event. We also release a fully annotated dataset consisting of single 4K camera and twelve-camera videos of basketball games.

关键词： Cameras Games Production Streaming media Robot vision systems Computational modeling

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iCub World: Friendly Robots Help Building Good vision Data-Sets

iCub World: Friendly Robots Help Building Good Vision Data-S...

引用

26th ieee conference on computer vision and pattern recognition (CVPR)

作者： Fanello, Sean Ryan Ciliberto, Carlo Santoro, Matteo Natale, Lorenzo Metta, Giorgio Rosasco, Lorenzo Odone, Francesca Ist Italiano Tecnol iCub Facil Genoa Italy Univ Genoa DIBRIS Genoa Italy Italian Inst Technol MIT LCSL I-16163 Genoa Italy

ISBN: (纸本)9780769549903

In this paper we present and start analyzing the iCub World data-set, an object recognition data-set, we acquired using a Human-Robot Interaction (HRI) scheme and the iCub humanoid robot platform. Our set up allows for rapid acquisition and annotation of data with corresponding ground truth. While more constrained in its scopes - the iCub world is essentially a robotics research lab - we demonstrate how the proposed data-set poses challenges to current recognition systems. The iCubWorld data-set is publicly available (1).

关键词： human-robot interaction humanoid robots object recognition robot vision

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Self Texture Transfer Networks for Low Bitrate Image Compression

Self Texture Transfer Networks for Low Bitrate Image Compres...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Iwai, Shoma Miyazaki, Tomo Sugaya, Yoshihiro Omachi, Shinichiro Tohoku Univ Grad Sch Engn Dept Commun Sendai Miyagi Japan

ISBN: (纸本)9781665448994

Lossy image compression causes a loss of texture, especially at low bitrate. To mitigate this problem, we propose a novel image compression method that utilizes a reference-based image super-resolution model. We use two image compression models and a self texture transfer model. The image compression models encode and decode a whole input image and selected reference patches. The reference patches are small but compressed with high quality. The self texture transfer model transfers the texture of reference patches into similar regions in the compressed image. The experimental results show that our method can reconstruct accurate texture by transferring the texture of reference patches.

关键词： computer vision Image coding conferences Bit rate Superresolution pattern recognition Image reconstruction

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Trusting the computer in computer vision: A Privacy-Affirming Framework 30

Trusting the Computer in Computer Vision: A Privacy-Affirmin...

引用

30th ieee/CVF conference on computer vision and pattern recognition workshops (cvprw)

作者： Chen, Andrew Tzer-Yeu Biglari-Abhari, Morteza Wang, Kevin I-Kai Univ Auckland Dept Elect & Comp Engn Embedded Syst Res Grp Auckland New Zealand

ISBN: (纸本)9781538607336

The use of surveillance cameras continues to increase, ranging from conventional applications such as law enforcement to newer scenarios with looser requirements such as gathering business intelligence. Humans still play an integral part in using and interpreting the footage from these systems, but are also a significant factor in causing unintentional privacy breaches. As computer vision methods continue to improve, we argue in this position paper that system designers should reconsider the role of machines in surveillance, and how automation can be used to help protect privacy. We explore this by discussing the impact of the human-in-the-loop, the potential for using abstraction and distributed computing to further privacy goals, and an approach for determining when video footage should be hidden from human users. We propose that in an ideal surveillance scenario, a privacy-affirming framework causes collected camera footage to be processed by computers directly, and never shown to humans. This implicitly requires humans to establish trust, to believe that computer vision systems can generate sufficiently accurate results without human supervision, so that if information about people must be gathered, unintentional data collection is mitigated as much as possible.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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