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International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Renlong Chen Mingjun Liu Xueyan Gong Jinping Li School of Information Science and Engineering Shandong Provincial Key Laboratory of Network Based Intelligent Computing (University of Jinan) Shandong College and University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-year Jinan China Qilu Institute of Technology Shandong Provincial Key Laboratory of Network Based Intelligent Computing (University of Jinan) Shandong College and University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-year Jinan China

ISBN: (纸本)9781728105529

In binocular vision, the pixel matching of no obvious feature refers to the matching of pixels in the area where the gray value does not change significantly or in the area where there is no significant gradient change in the gray level. The basic idea is that the position of the matching pixels in the area with no obvious features can be determined by the peripheral feature points. First, the camera is calibrated by the chessboard calibration method, and the internal and external parameters of the camera are obtained. The distortion of the left and right images is corrected by the calibration results. Then, the SIFT algorithm is used to extract the feature points, the Euclidean distance threshold is set to determine the better matching points, and the random sample consensus (RANSAC) algorithm is used to eliminate the wrong matching points. Finally, four target matching points with the same distance and direction information are obtained, and the final target matching points are obtained by averaging the four target matching points. Experiments show that any pair of pixels in the unmarked area can be matched accurately.

关键词： Binocular vision Pixel matching with no obvious feature area SIFT RANSAC Peripheral feature points Binocular vision pixel Feature Points Sifting gray value Cameras

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An Image Denoising Method Based on Deep Residual GAN

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Journal of Physics: Conference Series 2020年第3期1550卷

作者： Ziyuan Wang Lidan Wang Shukai Duan Yunfei Li Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing 400715 China Schoolof Electronic and information engineering Southwest University Chongqing400715 China College of Artificiall Intelligence Southwest University Chongqing 400715 China Braininspired Computing & Intelligent Control of Chongqing Key Lab Chongqing 400715 China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715 China

As people come into contact with image data more often, high quality and clear images attract more attention. Many methods have been proposed to deal with image noise problem including deep learning (DL). However most of them is lack of capability when customers want more perceptual details of the image without information loss. In this paper, a deep residual network based on generative adversarial (GAN) network was proposed to complete the image denoising mission. Firstly, a generative-adversarial network structure based on residual blocks was designed. Secondly, a refined loss function was given to train the GAN network. The well designed loss function can help the generated image to be very close to the clear counterpart (ground truth) while enhancing more details in colours and brightness. Finally, extensive experiments show that our network is not only convincing for images denoising, but also effective for other image process tasks, such as image defogging, medical CT denoising etc., presenting impressive and competitive effects.

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Hierarchy-dependent cross-platform multi-view feature learning for venue category prediction

arXiv

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arXiv 2018年

作者： Jiang, Shuqiang Min, Weiqing Mei, Shuhuan Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Shandong University of Science and Technology Shandong266590 China

In this work, we focus on visual venue category prediction, which can facilitate various applications for locationbased service and personalization. Considering that the complementarity of different media platforms, it is reasonable to leverage venue-relevant media data from different platforms to boost the prediction performance. Intuitively, recognizing one venue category involves multiple semantic cues, especially objects and scenes, and thus they should contribute together to venue category prediction. In addition, these venues can be organized in a natural hierarchical structure, which provides prior knowledge to guide venue category estimation. Taking these aspects into account, we propose a Hierarchy-dependent Cross-platform Multi-view Feature Learning (HCM-FL) framework for venue category prediction from videos by leveraging images from other platforms. HCM-FL includes two major components, namely Cross-Platform Transfer Deep Learning (CPTDL) and Multi-View Feature Learning with the Hierarchical Venue Structure (MVFL-HVS). CPTDL is capable of reinforcing the learned deep network from videos using images from other platforms. Specifically, CPTDL first trained a deep network using videos. These images from other platforms are filtered by the learnt network and these selected images are then fed into this learnt network to enhance it. Two kinds of pre-Trained networks on the ImageNet and Places dataset are employed. Therefore, we can harness both object-oriented and scene-oriented deep features through these enhanced deep networks. MVFL-HVS is then developed to enable multi-view feature fusion. It is capable of embedding the hierarchical structure ontology to support more discriminative joint feature learning. We conduct the experiment on videos from Vine and images from Foursqure. These experimental results demonstrate the advantage of our proposed framework in jointly utilizing multi-platform data, multi-view deep features and hierarchical venue structure kno

关键词： Forecasting

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CASICT-DCU neural machine translation systems for WMT17 2

CASICT-DCU neural machine translation systems for WMT17

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2nd Conference on Machine Translation, WMT 2017

作者： Zhang, Jinchao Porkaew, Peerachet Hu, Jiawei Zhao, Qiuye Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China ADAPT Centre School of Computing Dublin City University Ireland

ISBN: (纸本)9781945626968

We participated in the WMT 2016 shared news translation task on English ? Chinese language pair. Our systems are based on the encoder-decoder neural machine translation model with the attention mechanism. We employ the Gated Recurrent Unit (GRU) with the linear associative connection to build deep encoder and address the unknown words with the dictionary replace approach. The dictionaries are extracted from the parallel training data with unsupervised word alignment method. In the decoding procedure, the translation probabilities of the target word from different models are averagely combined as the ensemble strategy. In this paper, we introduce our systems from data preprocessing to post-editing in details. © 2017 Association for Computational Linguistics

关键词： Neural machine translation

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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Lo-Regularized Hybrid Gradient Sparsity Priors for Robust Single-Image Blind Deblurring

Lo-Regularized Hybrid Gradient Sparsity Priors for Robust Si...

引用

2018 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2018

作者： Liu, Ryan Wen Yin, Wei Xiang, Shengwu Peng, Silang School of Navigation Wuhan University of Technology Wuhan430063 China Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing210094 China School of Computer Science and Technology Wuhan University of Technology Wuhan430070 China Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781538646588

Single-image blind deblurring is a challenging ill-posed inverse problem which aims to estimate both blur kernel and latent sharp image from only one observation. This paper focuses on first estimating the blur kernel alone and then restoring the latent image since it has been proven to be more feasible to handle the ill-posed nature during blind deblurring. To estimate an accurate blur kernel, L0-norm of both first-and second-order image gradients is proposed to regularize the final estimation result. The proposed L0-regularized hybrid gradient sparsity priors obtain major benefit from the intrinsic sparsity properties of images and can assist in guaranteeing high-quality blur kernel estimation. Once the blur kernel is estimated, the final clean image is robustly generated using the combination of L1-norm data-fidelity term and total variation regularizer. Experimental results have demonstrated the satisfactory performance of the proposed method. © 2018 IEEE.

关键词： Kernel Estimation Robustness Optimization Image restoration Deconvolution Convolution"

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Blend: A novel combined MT metric based on direct assessment - CASICT-DCU submission to WMT17 metrics task 2

Blend: A novel combined MT metric based on direct assessment...

引用

2nd Conference on Machine Translation, WMT 2017

作者： Ma, Qingsong Graham, Yvette Wang, Shugen Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology University of Chinese Academy of Sciences China ADAPT Centre School of Computing Dublin City University Ireland

ISBN: (纸本)9781945626968

Existing metrics to evaluate the quality of Machine Translation hypotheses take different perspectives into account. DPMFcomb, a metric combining the merits of a range of metrics, achieved the best performance for evaluation of to-English language pairs in the previous two years of WMT Metrics Shared Tasks. This year, we submit a novel combined metric, Blend, to WMT17 Metrics task. Compared to DPMFcomb, Blend includes the following adaptations: i) We use DA human evaluation to guide the training process with a vast reduction in required training data, while still achieving improved performance when evaluated on WMT16 to-English language pairs;ii) We carry out experiments to explore the contribution of metrics incorporated in Blend, in order to find a trade-off between performance and efficiency. © 2017 Association for Computational Linguistics

关键词： Economic and social effects

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Correction to: Predicting protein inter-residue contacts using composite likelihood maximization and deep learning

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BMC bioinformatics 2019年第1期20卷 616页

作者： Haicang Zhang Qi Zhang Fusong Ju Jianwei Zhu Yujuan Gao Ziwei Xie Minghua Deng Shiwei Sun Wei-Mou Zheng Dongbo Bu Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China. University of Chinese Academy of Sciences Beijing China. Center for Quantitative Biology School of Mathematical Sciences Center for Statistical Sciences Peking University Beijing China. College of Life Science and Technology Huazhong University of Science and Technology Wuhan China. Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China. dwsun@***. Institute of Theoretical Physics Chinese Academy of Sciences Beijing China. zheng@***. Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China. dbu@***. University of Chinese Academy of Sciences Beijing China. dbu@***.

Following publication of the original article [1], the author explained that there are several errors in the original article.

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Why is the Winner the Best?

Why is the Winner the Best?

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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.

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A survey on food computing

arXiv

引用

arXiv 2018年

作者： Min, Weiqing Jiang, Shuqiang Liu, Linhu Rui, Yong Jain, Ramesh Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China Lenovo Group No. 6 Shangdi West Road Beijing China Department of Computer Science University of California IrvineCA United States

Food is very essential for human life and it is fundamental to the human experience. Food-related study may support multifarious applications and services, such as guiding the human behavior, improving the human health and understanding the culinary culture. With the rapid development of social networks, mobile networks, and Internet of Things (IoT), people commonly upload, share, and record food images, recipes, cooking videos, and food diaries, leading to large-scale food data. Large-scale food data offers rich knowledge about food and can help tackle many central issues of human society. Therefore, it is time to group several disparate issues related to food computing. Food computing acquires and analyzes heterogenous food data from disparate sources for perception, recognition, retrieval, recommendation, and monitoring of food. In food computing, computational approaches are applied to address food related issues in medicine, biology, gastronomy and agronomy. Both large-scale food data and recent breakthroughs in computer science are transforming the way we analyze food data. Therefore, vast amounts of work has been conducted in the food area, targeting different food-oriented tasks and applications. However, there are very few systematic reviews, which shape this area well and provide a comprehensive and in-depth summary of current efforts or detail open problems in this area. In this paper, we formalize food computing and present such a comprehensive overview of various emerging concepts, methods, and tasks. We summarize key challenges and future directions ahead for food computing. This is the first comprehensive survey that targets the study of computing technology for the food area and also offers a collection of research studies and technologies to benefit researchers and practitioners working in different food-related fields. Copyright © 2018, The Authors. All rights reserved.

关键词： Surveys

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