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25th IEEE International Conference on Image processing, ICIP 2018

作者： Onur Ozyurt, Erdem Gunsel, Bilge Multimedia Signal Proc. and Pattern Recognition Lab. Istanbul Technical University Turkey

ISBN: (纸本)9781479970612

We propose an object tracking method for Wide Area Motion Imagery (WAMI) video sequences, which models the tracking as a regularization problem through sparse representation of aerial video content. The proposed object tracker, Ll Dpct, applies particle filter tracking, and unlike the existing methods, it integrates a deep-learning-based object detector into the regularization scheme to improve the tracking performance. In order to enhance robustness to occlusion and scale changes, Ll Dpct monitors the state propagation, the level of sparsity as well as the representation capability of the model and receives feedback from the detector to update the observation model of the particle filter. Ll Dpct incrementally updates the dictionary of the sparse representation that enables us to efficiently represent the appearance changes of the object arising from illumination changes and high motion. Numerical results obtained on commonly used VIVID and UAV123 datasets denote that Ll Dpct significantly improves the object tracking performance in terms of precision rate and success rate compared to the state-of-the-art trackers. © 2018 IEEE.

关键词： Antennas

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AIM 2020 Challenge on Image Extreme Inpainting 16th

AIM 2020 Challenge on Image Extreme Inpainting

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Workshops held at the 16th European Conference on Computer Vision, ECCV 2020

作者： Ntavelis, Evangelos Romero, Andrés Bigdeli, Siavash Timofte, Radu Hui, Zheng Wang, Xiumei Gao, Xinbo Shin, Chajin Kim, Taeoh Son, Hanbin Lee, Sangyoun Li, Chao Li, Fu He, Dongliang Wen, Shilei Ding, Errui Bai, Mengmeng Li, Shuchen Zeng, Yu Lin, Zhe Yang, Jimei Zhang, Jianming Shechtman, Eli Lu, Huchuan Zeng, Weijian Ni, Haopeng Cai, Yiyang Li, Chenghua Xu, Dejia Wu, Haoning Han, Yu Nadim, Uddin S. M. Jang, Hae Woong Ahmed, Soikat Hasan Yoon, Jungmin Jung, Yong Ju Li, Chu-Tak Liu, Zhi-Song Wang, Li-Wen Siu, Wan-Chi Lun, Daniel P. K. Suin, Maitreya Purohit, Kuldeep Rajagopalan, A.N. Narang, Pratik Mandal, Murari Chauhan, Pranjal Singh Computer Vision Lab ETH Zürich Zürich Switzerland CSEM Neuchâtel Switzerland School of Electronic Engineering Xidian University Xi’an China Image and Video Pattern Recognition Laboratory School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Baidu Inc. Beijing China Beijing China Dalian University of Technology Dalian China Adobe San Jose United States Rensselaer Polytechnic Institute Troy United States Peking University Beijing China Lab Gachon University Seongnam Korea Republic of Centre for Multimedia Signal Processing Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong China Indian Institute of Technology Madras Chennai India BITS Pilani Pilani India MNIT Jaipur Jaipur India

ISBN: (纸本)9783030670696

This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting. The goal of track 1 is to inpaint large part of the image with no supervision. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the input. The challenge had 88 and 74 participants, respectively. 11 and 6 teams competed in the final phase of the challenge, respectively. This report gauges current solutions and set a benchmark for future extreme image inpainting methods. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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WAMI OBJECT TRACKING USING L_1 TRACKER INTEGRATED WITH A DEEP DETECTOR

WAMI OBJECT TRACKING USING L_1 TRACKER INTEGRATED WITH A DEE...

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IEEE International Conference on Image processing

作者： Erdem Onur Ozyurt Bilge Gunsel Multimedia Signal Proc. and Pattern Recognition Lab. Istanbul Technical University Turkey

We propose an object tracking method for Wide Area Motion Imagery (WAMI) video sequences, which models the tracking as a regularization problem through sparse representation of aerial video content. The proposed object tracker, L1Dpct, applies particle filter tracking, and unlike the existing methods, it integrates a deep-learning-based object detector into the regularization scheme to improve the tracking performance. In order to enhance robustness to occlusion and scale changes, L1Dpct monitors the state propagation, the level of sparsity as well as the representation capability of the model and receives feedback from the detector to update the observation model of the particle filter. L1Dpct incrementally updates the dictionary of the sparse representation that enables us to efficiently represent the appearance changes of the object arising from illumination changes and high motion. Numerical results obtained on commonly used VIVID and UAV123 datasets denote that L1Dpct significantly improves the object tracking performance in terms of precision rate and success rate compared to the state-of-the-art trackers.

关键词： Target tracking Detectors Dictionaries Object tracking Monitoring Minimization

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Semi-heterogeneous three-way joint embedding network for sketch-based image retrieval

arXiv

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arXiv 2019年

作者： Lei, Jianjun Song, Yuxin Peng, Bo Ma, Zhanyu Shao, Ling Song, Yi-Zhe School of Electrical and Information Engineering Tianjin University Tianjin300072 China Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing100876 China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates SketchX Lab Centre for Vision Speech and Signal Processing University of Surrey Surrey GuildfordGU2 7XH United Kingdom

Sketch-based image retrieval (SBIR) is a challenging task due to the large cross-domain gap between sketches and natural images. How to align abstract sketches and natural images into a common high-level semantic space remains a key problem in SBIR. In this paper, we propose a novel semi-heterogeneous three-way joint embedding network (Semi3-Net), which integrates three branches (a sketch branch, a natural image branch, and an edgemap branch) to learn more discriminative cross-domain feature representations for the SBIR task. The key insight lies with how we cultivate the mutual and subtle relationships amongst the sketches, natural images, and edgemaps. A semi-heterogeneous feature mapping is designed to extract bottom features from each domain, where the sketch and edgemap branches are shared while the natural image branch is heterogeneous to the other branches. In addition, a joint semantic embedding is introduced to embed the features from different domains into a common high-level semantic space, where all of the three branches are shared. To further capture informative features common to both natural images and the corresponding edgemaps, a co-attention model is introduced to conduct common channel-wise feature recalibration between different domains. A hybrid-loss mechanism is designed to align the three branches, where an alignment loss and a sketch-edgemap contrastive loss are presented to encourage the network to learn invariant cross-domain representations. Experimental results on two widely used category-level datasets (Sketchy and TU-Berlin Extension) demonstrate that the proposed method outperforms state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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Image quality analysis of limited angle tomography using the shift-variant data loss model

Image quality analysis of limited angle tomography using the...

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Workshops on Image processing for the medicine, 2016

作者： Huang, Yixing Lauritsch, Guenter Amrehn, Mario Taubmann, Oliver Haase, Viktor Stromer, Daniel Huang, Xiaolin Maier, Andreas Pattern Recognition Lab Friedrich-Alexander-University Erlangen-Nuremberg Germany Siemens Healthcare GmbH Forchheim Germany Institute for Signal Processing University of Lübeck Germany

ISBN: (纸本)9783662494646

This paper investigates the application of the shift-variant data loss (SVDL) model in image quality assessment for a state-of-the-art reconstruction technique, the weighted total variation (wTV), in limited angle tomography. The SVDL model is used to analyze the acquired frequency information in 2D fan-beam limited angle tomography. The wTV algorithm is applied to reconstruct some specific mathematical phantoms. The experiments show that the reconstructed image quality depends on the relation of the source trajectory and geometric structure of the imaged object, position, shape, size and orientation in particular. © Springer-Verlag Berlin Heidelberg 2016.

关键词： Image quality

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Make the most of time temporal extension of the iTV algorithm for 4D cardiac C-arm CT

Make the most of time temporal extension of the iTV algorith...

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Workshops on Image processing for the medicine, 2016

作者： Haase, Viktor Taubmann, Oliver Huang, Yixing Krings, Gregor Lauritsch, Günter Maier, Andreas Mertins, Alfred Institute for Signal Processing University of Luebeck Germany Pattern Recognition Lab Friedrich-Alexander-University Erlangen-Nuremberg Germany Erlangen Germany University Medical Center Utrecht Netherlands Siemens Healthcare GmbH Forchheim Germany

ISBN: (纸本)9783662494646

Gated 4D cardiac imaging with C-arm CT scanners suffers from insufficient image quality due to strong angular undersampling. To deal with this problem, we suggest an iterative reconstruction method with spatial and temporal total variation regularization based on an established framework which controls the relative contributions of raw data error minimization and regularization. This new method is tested on a simulated heart phantom and on two clinical data sets. We show that the additional use of temporal regularization is advantageous compared to spatial regularization exclusively, with the relative root mean square error lowered from 11.75 % to 8.24 % in the phantom study. © Springer-Verlag Berlin Heidelberg 2016.

关键词： Phantoms

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A novel panoramic image stitching algorithm based on ORB

A novel panoramic image stitching algorithm based on ORB

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2017 IEEE International Conference on Applied System Innovation, ICASI 2017

作者： Wang, Maosen Niu, Shaozhang Yang, Xuan Beijing Key Lab of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing100876 China Institute of Image Processing and Pattern Recognition North China University of Technology No.5 Jinyuanzhuang Road Shijingshan District Beijing China

ISBN: (纸本)9781509048977

Image stitching technique is to integrate multiple images with overlapping regions into a complete image with a wide viewing angle, less distortion, and no obvious suture. Image stitching could be used for global positioning and robot autonomous navigation without changing the hardware. SIFT feature and SURF feature are the classical algorithm in the image stitching. But they have the long time-consuming problem. Facing this problem, a novel fast image stitching algorithm based on ORB (Oriented FAST and Rotated BRIEF) features is proposed in this paper. The algorithm firstly selects ORB algorithm which adds the direction information to the FAST detector for image feature extracting and matching. Secondly, RASANC (Random Sample Consensus) algorithm is used to eliminate false matching points. Finally, weighted average method is used to speed up the image fusion. Experimental results show that the stitching effect of the algorithm in this paper is the same as that of SIFT algorithm and SURF algorithm. The two obvious advantages of our algorithm include: (1) stitching speed is much faster than that of Shift and Surf;(2)The algorithm is robust to external parameters of camera. So the proposed algorithm is hopefully applied to some handed electrical devices. © 2017 IEEE.

关键词： Image fusion

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Benchmarking super-resolution algorithms on real data

arXiv

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arXiv 2017年

作者： Köhler, Thomas Bätz, Michel Naderi, Farzad Kaup, André Maier, Andreas K. Riess, Christian Pattern Recognition Lab Dept. of Computer Science Multimedia Communications and Signal Processing Dept. of Electrical Electronic and Communication Engineering Erlangen-Nürnberg Erlangen Germany

Over the past decades, various super-resolution (SR) techniques have been developed to enhance the spatial resolution of digital images. Despite the great number of methodical contributions, there is still a lack of comparative validations of SR under practical conditions, as capturing real ground truth data is a challenging task. Therefore, current studies are either evaluated 1) on simulated data or 2) on real data without a pixel-wise ground truth. To facilitate comprehensive studies, this paper introduces the publicly available Super-Resolution Erlangen (SupER) database that includes real low-resolution images along with high-resolution ground truth data. Our database comprises image sequences with more than 20k images captured from 14 scenes under various types of motions and photometric conditions. The datasets cover four spatial resolution levels using camera hardware binning. With this database, we benchmark 15 single-image and multi-frame SR algorithms. Our experiments quantitatively analyze SR accuracy and robustness under realistic conditions including independent object and camera motion or photometric variations. Copyright © 2017, The Authors. All rights reserved.

关键词： Database systems

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The INTERSPEECH 2018 computational paralinguistics challenge: Atypical & self-assessed affect, crying & Heart beats 19

The INTERSPEECH 2018 computational paralinguistics challenge...

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19th Annual Conference of the International Speech Communication, INTERSPEECH 2018

作者： Schuller, Björn W. Steidl, Stefan Batliner, Anton Marschik, Peter B. Baumeister, Harald Dong, Fengquan Hantke, Simone Pokorny, Florian B. Rathner, Eva-Maria Bartl-Pokorny, Katrin D. Einspieler, Christa Zhang, Dajie Baird, Alice Amiriparian, Shahin Qian, Kun Ren, Zhao Schmitt, Maximilian Tzirakis, Panagiotis Zafeiriou, Stefanos GLAM - Group on Language Audio and Music Imperial College London United Kingdom ZD.B Department of Embedded Intelligence for Health Care and Wellbeing University of Augsburg Germany audEERING GmbH Gilching Germany Pattern Recognition Lab FAU Erlangen-Nuremberg Germany iDN - interdisciplinary Developmental Neuroscience Medical University of Graz Austria University Medical Center Göttingen Germany Department of Women's and Children's Health Karolinska Institutet Stockholm Sweden Department of Clinical Psychology and Psychotherapy University of Ulm Germany Shenzhen University General Hospital Shenzhen China Machine Intelligence and Signal Processing Group Technische Universität München Germany University of Oulu Finland

The INTERSPEECH 2018 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Atypical Affect Sub-Challenge, four basic emotions annotated in the speech of handicapped subjects have to be classified;in the Self-Assessed Affect Sub-Challenge, valence scores given by the speakers themselves are used for a three-class classification problem;in the Crying Sub-Challenge, three types of infant vocalisations have to be told apart;and in the Heart Beats Sub-Challenge, three different types of heart beats have to be determined. We describe the Sub-Challenges, their conditions, and baseline feature extraction and classifiers, which include data-learnt (supervised) feature representations by end-to-end learning, the 'usual' ComParE and BoAW features, and deep unsupervised representation learning using the AUDEEP toolkit for the first time in the challenge series. © 2018 International Speech Communication Association. All rights reserved.

关键词： Heart

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Cross-modal subspace learning for fine-grained sketch-based image retrieval

arXiv

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arXiv 2017年

作者： Xu, Peng Yin, Qiyue Huang, Yongye Song, Yi-Zhe Ma, Zhanyu Wang, Liang Xiang, Tao Bastiaan Kleijn, W. Guo, Jun Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China National Lab of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China SketchX Lab School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Communications and Signal Processing Group Victoria University of Wellington New Zealand

Sketch-based image retrieval (SBIR) is challenging due to the inherent domain-gap between sketch and photo. Compared with pixel-perfect depictions of photos, sketches are iconic renderings of the real world with highly abstract. Therefore, matching sketch and photo directly using low-level visual clues are unsufficient, since a common low-level subspace that traverses semantically across the two modalities is non-trivial to establish. Most existing SBIR studies do not directly tackle this cross-modal problem. This naturally motivates us to explore the effectiveness of cross-modal retrieval methods in SBIR, which have been applied in the image-text matching successfully. In this paper, we introduce and compare a series of state-of-the-art cross-modal subspace learning methods and benchmark them on two recently released fine-grained SBIR datasets. Through thorough examination of the experimental results, we have demonstrated that the subspace learning can effectively model the sketch-photo domain-gap. In addition we draw a few key insights to drive future research. Copyright © 2017, The Authors. All rights reserved.

关键词： Abstracting

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