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What Will I Do Next? The Intention from Motion Experiment 30

What Will I Do Next? The Intention from Motion Experiment

引用

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

作者： Zunino, Andrea Cavazza, Jacopo Koul, Atesh Cavallo, Andrea Becchio, Cristina Murino, Vittorio Ist Italiano Tecnol Pattern Anal & Comp Vis PAVIS Genoa Italy Ist Italiano Tecnol Robot Brain & Cognit Sci Genoa Italy Univ Genoa Elect Elect & Telecommun Engn & Naval Architectur Genoa Italy Univ Turin Psychol Dept Turin Italy Univ Verona Comp Sci Dept Verona Italy

ISBN: (纸本)9781538607336

In computer vision, video-based approaches have been widely explored for the early classification and the prediction of actions or activities. However, it remains unclear whether this modality (as compared to 3D kinematics) can still be reliable for the prediction of human intentions, defined as the overarching goal embedded in an action sequence. Since the same action can be performed with different intentions, this problem is more challenging but yet affordable as proved by quantitative cognitive studies which exploit the 3D kinematics acquired through motion capture systems. In this paper, we bridge cognitive and computer vision studies, by demonstrating the effectiveness of video-based approaches for the prediction of human intentions. Precisely, we propose Intention from Motion, a new paradigm where, without using any contextual information, we consider instantaneous grasping motor acts involving a bottle in order to forecast why the bottle itself has been reached (to pass it or to place in a box, or to pour or to drink the liquid inside). We process only the grasping onsets casting intention prediction as a classification framework. Leveraging on our multimodal acquisition (3D motion capture data and 2D optical videos), we compare the most commonly used 3D descriptors from cognitive studies with state-of-the-art video-based techniques. Since the two analyses achieve an equivalent performance, we demonstrate that computer vision tools are effective in capturing the kinematics and facing the cognitive problem of human intention prediction.

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

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NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results 30

NTIRE 2017 Challenge on Single Image Super-Resolution: Metho...

引用

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

作者： Timofte, Radu Agustsson, Eirikur Van Gool, Luc Yang, Ming-Hsuan Zhang, Lei Lim, Bee Son, Sanghyun Kim, Heewon Nah, Seungjun Lee, Kyoung Mu Wang, Xintao Tian, Yapeng Yu, Ke Zhang, Yulun Wu, Shixiang Dong, Chao Lin, Liang Qiao, Yu Loy, Chen Change Bae, Woong Yoo, Jaejun Han, Yoseob Ye, Jong Chul Choi, Jae-Seok Kim, Munchurl Fan, Yuchen Yu, Jiahui Han, Wei Liu, Ding Yu, Haichao Wang, Zhangyang Shi, Honghui Wang, Xinchao Huang, Thomas S. Chen, Yunjin Zhang, Kai Zuo, Wangmeng Tang, Zhimin Luo, Linkai Li, Shaohui Fu, Min Cao, Lei Heng, Wen Bui, Giang Truc Le Duan, Ye Tao, Dacheng Wang, Ruxin Lin, Xu Pang, Jianxin Xu, Jinchang Zhao, Yu Xu, Xiangyu Pan, Jinshan Sun, Deqing Zhang, Yujin Song, Xibin Dai, Yuchao Qin, Xueying Xuan-Phung Huynh Guo, Tiantong Mousavi, Hojjat Seyed Tiep Huu Vu Monga, Vishal Cruz, Cristovao Egiazarian, Karen Katkovnik, Vladimir Mehta, Rakesh Jain, Arnav Kumar Agarwalla, Abhinav Praveen, Ch V. Sai Zhou, Ruofan Wen, Hongdiao Zhu, Che Xia, Zhiqiang Wang, Zhengtao Guo, Qi ETH Comp Vision Lab Zurich Switzerland Merantix GmbH Berlin Germany Katholieke Univ Leuven ESAT Leuven Belgium Univ Calif Merced Merced CA USA Polytech Univ Hong Kong Hong Kong Hong Kong Peoples R China Seoul Natl Univ CVLab Seoul South Korea Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Hong Kong Hong Kong Peoples R China SenseTime Hong Kong Hong Kong Peoples R China Korea Adv Inst Sci & Technol Daejeon South Korea Univ Illinois IFP Urbana IL USA ULSee Inc Hong Kong Hong Kong Peoples R China Harbin Inst Technol Harbin Heilongjiang Peoples R China Xiamen Univ Xiamen Peoples R China Peking Univ Beijing Peoples R China Univ Missouri Columbia MO 65211 USA CSIC Kunming Linghu Environm Intelligent Sensing Kunming Yunnan Peoples R China Yunshangyun Artificial Intelligence Inst Shenzhen Peoples R China ShenzhenUnion Vis Innovat Technol Co L Shenzhen Peoples R China Ubtech Robot Corp Shenzhen Peoples R China Univ Sydney UBTech Sydney Artificial Intelligence Inst Sydney NSW Australia Beijing Univ Posts & Telecommun Beijing Peoples R China Tsinghua Univ Elect Engn Beijing Peoples R China Univ Calif Merced EECS Merced CA USA Shandong Univ Jinan Shandong Peoples R China Australian Natl Univ Canberra ACT Australia Sejong Univ Seoul South Korea Penn State Univ University Pk PA 16802 USA Tampere Univ Technol Tampere Finland Indian Inst Technol Kharagpur Kharagpur W Bengal India Ecole Polytech Fed Lausanne Lausanne Switzerland Univ Elect Sci & Technol China Ctr Robot Sch Elect Engn Chengdu Sichuan Peoples R China

ISBN: (纸本)9781538607336

This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results. A new DIVerse 2K resolution image dataset (DIV2K) was employed. The challenge had 6 competitions divided into 2 tracks with 3 magnification factors each. Track 1 employed the standard bicubic downscaling setup, while Track 2 had unknown downscaling operators (blur kernel and decimation) but learnable through low and high res train images. Each competition had similar to 100 registered participants and 20 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution.

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

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NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results

NTIRE 2018 Challenge on Single Image Super-Resolution: Metho...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Radu Timofte Shuhang Gu Jiqing Wu Luc Van Gool Lei Zhang Ming-Hsuan Yang Muhammad Haris Greg Shakhnarovich Norimichi Ukita Shijia Hu Yijie Bei Zheng Hui Xiao Jiang Yanan Gu Jie Liu Yifan Wang Federico Perazzi Brian McWilliams Alexander Sorkine-Hornung Olga Sorkine-Hornung Christopher Schroers Jiahui Yu Yuchen Fan Jianchao Yang Ning Xu Zhaowen Wang Xinchao Wang Thomas S. Huang Xintao Wang Ke Yu Tak-Wai Hui Chao Dong Liang Lin Chen Change Loy Dongwon Park Kwanyoung Kim Se Young Chun Kai Zhang Pengjv Liu Wangmeng Zuo Shi Guo Jiye Liu Jinchang Xu Yijiao Liu Fengye Xiong Yuan Dong Hongliang Bai Alexandru Damian Nikhil Ravi Sachit Menon Cynthia Rudin Junghoon Seo Taegyun Jeon Jamyoung Koo Seunghyun Jeon Soo Ye Kim Jae-Seok Choi Sehwan Ki Soomin Seo Hyeonjun Sim Saehun Kim Munchurl Kim Rong Chen Kun Zeng Jinkang Guo Yanyun Qu Cuihua Li Namhyuk Ahn Byungkon Kang Kyung-Ah Sohn Yuan Yuan Jiawei Zhang Jiahao Pang Xiangyu Xu Yan Zhao Wei Deng Sibt Ul Hussain Muneeb Aadil Rafia Rahim Xiaowang Cai Fang Huang Yueshu Xu Pablo Navarrete Michelini Dan Zhu Hanwen Liu Jun-Hyuk Kim Jong-Seok Lee Yiwen Huang Ming Qiu Liting Jing Jiehang Zeng Ying Wang Manoj Sharma Rudrabha Mukhopadhyay Avinash Upadhyay Sriharsha Koundinya Ankit Shukla Santanu Chaudhury Zhe Zhang Yu Hen Hu Lingzhi Fu ETH Zurich Zurich Switzerland Merantix Germany NTIRE Eidgenossische Technische Hochschule Zurich Zurich ZH CH ESAT KU Leuven Belgium University of California at Merced CA USA Toyota Technological Institute TTI-Chicago Chicago US Duke University US School of Electronic Engineering Xidian University China Disney Research Facebook Oculus University of Illinois at Urbana-Champaign Illinois US Snap Inc. Snap Inc. Adobe Research Stevens Institute of Technology US University of Illinois at Urbana-Champaign US The Chinese University of Hong Kong Hong Kong China SenseTime Research School of Electrical and Computer Engineering National Institute of Science and Technolog Korea School of Electrical and Computer Engineering National Institute of Science and Technology Korea School of Computer Science and Technology Harbin Institute of Technology Harbin China Beijing University of Posts and Telecommunications China Beijing Faceall Technology Co. Ltd China Satrec Initiative R&D Center Korea Advanced Institute of Science and Technology Korea Xiamen University China Xiamen Universi China Ajou University Korea Electrical and Computer Engineering University of Alberta Canada Fuzhou University China National University of Computer & Emerging Sciences Islamabad Pakistan BOE Technology Group Co. Ltd. Yonsei University Korea Wenhua College China Software School Xiamen University China CSIR-CEERI India University of Wisconsin-Madison Madison Wisconsin US Institute of Integrated Automation School of Electronic and Information Engineering Xian Jiaotong University China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661017

This paper reviews the 2nd NTIRE challenge on single image super-resolution (restoration of rich details in a low resolution image) with focus on proposed solutions and results. The challenge had 4 tracks. Track 1 employed the standard bicubic downscaling setup, while Tracks 2, 3 and 4 had realistic unknown downgrading operators simulating camera image acquisition pipeline. The operators were learnable through provided pairs of low and high resolution train images. The tracks had 145, 114, 101, and 113 registered participants, resp., and 31 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution.

关键词： Image resolution Degradation Training Runtime computer architecture Testing Tracking

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Separating Texture and Illumination for Single-Shot Structured Light Reconstruction

Separating Texture and Illumination for Single-Shot Structur...

引用

ieee conference on computer vision and pattern recognition (CVPR)

作者： Vo, Minh Narasimhan, Srinivasa G. Sheikh, Yaser Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA

ISBN: (纸本)9781479943098

Active illumination based methods have a trade-off between acquisition time and resolution of the estimated 3D shapes. Multi-shot approaches can generate dense reconstructions but require stationary scenes. In contrast, singleshot methods are applicable to dynamic objects but can only estimate sparse reconstructions and are sensitive to surface texture. In this work, we develop a single-shot approach to produce dense reconstructions of highly textured objects. The key to our approach is an image decomposition scheme that can recover the illumination and the texture images from their mixed appearance. Despite the complex appearances of the illuminated textured regions, our method can accurately compute per pixel warps from the illumination pattern and the texture template to the observed image. The texture template is obtained by interleaving the projection sequence with an all-white pattern. Our estimated warping functions are reliable even with infrequent interleaved projection. Thus, we obtain detailed shape reconstruction and dense motion tracking of the textured surfaces. We validate the approach on synthetic and real data containing subtle non-rigid surface deformations.

关键词： Lighting illumination single-shot GRAIN pattern Image decomposition surface texture Reconstructive Surgical Procedures shape reconfiguration texture acquisition time structured light Reconstructions Dense

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Robust Three-view Triangulation Done Fast

Robust Three-view Triangulation Done Fast

引用

ieee conference on computer vision and pattern recognition (CVPR)

作者： Hedborg, Johan Robinson, Andreas Felsberg, Michael Linkoping Univ S-58183 Linkoping Sweden

ISBN: (纸本)9781479943098

Estimating the position of a 3-dimensional world point given its 2-dimensional projections in a set of images is a key component in numerous computer vision systems. There are several methods dealing with this problem, ranging from sub-optimal, linear least square triangulation in two views, to finding the world point that minimized the L-2-reprojection error in three views. This leads to the statistically optimal estimate under the assumption of Gaussian noise. In this paper we present a solution to the optimal triangulation in three views. The standard approach for solving the three-view triangulation problem is to find a closed-form solution. In contrast to this, we propose a new method based on an iterative scheme. The method is rigorously tested on both synthetic and real image data with corresponding ground truth, on a midrange desktop PC and a Raspberry Pi, a low-end mobile platform. We are able to improve the precision achieved by the closed-form solvers and reach a speed-up of two orders of magnitude compared to the current state-of-the-art solver. In numbers, this amounts to around 300K triangulations per second on the PC and 30K triangulations per second on Raspberry Pi.

关键词： Nonlinear optimization Structure from motion Three-view Triangulation Raspberries Solvers Fasting ground truth image data computer vision Systems Personal computers triangulation Gaussian noise

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