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Layered image compression using scalable auto-encoder

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arXiv 2019年

作者： Jia, Chuanmin Liu, Zhaoyi Wang, Yao Ma, Siwei Gao, Wen Department of Electrical and Computer Engineering Tandon School of Engineering New York University BrooklynNY11201 United States Peking University Institute of Digital Media Beijing100871 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China

This paper presents a novel convolutional neural network (CNN) based image compression framework via scalable auto-encoder (SAE). Specifically, our SAE based deep image codec consists of hierarchical coding layers, each of which is an end-to-end optimized auto-encoder. The coarse image content and texture are encoded through the first (base) layer while the consecutive (enhance) layers iteratively code the pixel-level reconstruction errors between the original and former reconstructed images. The proposed SAE structure alleviates the need to train multiple models for different bit-rate points by recently proposed auto-encoder based codecs. The SAE layers can be combined to realize multiple rate points, or to produce a scalable stream. The proposed method has similar rate-distortion performance in the low-to-medium rate range as the state-of-the-art CNN based image codec (which uses different optimized networks to realize different bit rates) over a standard public image dataset. Furthermore, the proposed codec generates better perceptual quality in this bit rate range. Copyright © 2019, The Authors. All rights reserved.

关键词： Image enhancement

Inspiremeposing: Learn pose and composition from portrait examples 26

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Inspiremeposing: Learn pose and composition from portrait ex...

26th Pacific Conference on computer Graphics and Applications, Pacific Graphics 2018

作者： Sheng, Bin Jin, Yuxi Li, Ping Wang, Wenxiao Fu, Hongbo Wu, Enhua Shanghai Jiao Tong University Department of Computer Science and Engineering China Macau University of Science and Technology Faculty of Information Technology China City University of Hong Kong School of Creative Media Hong Kong University of Macau Faculty of Science and Technology China Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software China

ISBN: (纸本)9783038680734

Since people tend to build relationship with others by personal photography, capturing high quality photographs on mobile device has become a strong demand. We propose a portrait photography guidance system to guide user’s photographing. We consider current scene image as our input and find professional photograph examples with similar aesthetic features for it. Deep residual network is introduced to gather scene classification information and represent common photograph rules by features, and random forest is adopted to establishing mapping relations between extracted features and examples. Besides, we implement our guidance system on a camera application and evaluate it by user study. © 2018 The Author(s).

关键词： Photography

Music, Computing, and Health: A Roadmap for the Current and Future Roles of Music technology for Health Care and Well-Being

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Music and Science 2021年 4卷

作者： Agres, Kat R. Schaefer, Rebecca S. Volk, Anja van Hooren, Susan Holzapfel, Andre Dalla Bella, Simone Müller, Meinard de Witte, Martina Herremans, Dorien Ramirez Melendez, Rafael Neerincx, Mark Ruiz, Sebastian Meredith, David Dimitriadis, Theo Magee, Wendy L. Yong Siew Toh Conservatory of Music National University of Singapore Singapore Social and Cognitive Computing Department Institute of High Performance Computing A*STAR Singapore Institute for Psychology Health Medical Neuropsychology Unit Leiden University Leiden Netherlands Leiden Institute for Brain and Cognition Leiden University Leiden Netherlands Academy of Creative and Performing Arts Leiden University Leiden Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Faculty of Health care Department of Arts Therapies Zuyd University of Applied Sciences Heerlen Netherlands KenVaK Research Centre for the Arts Therapies and Psychomotricity Heerlen Netherlands Faculty of Psychology Open University of The Netherlands Heerlen Netherlands Division of Media Technology and Interaction Design KTH Royal Institute of Technology Stockholm Sweden International Laboratory for Brain Music and Sound Research (BRAMS) Outremont QC Canada Department of Psychology University of Montreal Montreal QC Canada Centre for Research on Brain Language and Music (CRBLM) Montreal QC Canada University of Economics and Human Sciences in Warsaw Warsaw Poland International Audio Laboratories Erlangen Friedrich-Alexander Universität Erlangen-Nürnberg Erlangen Germany HAN University of Applied Sciences Department of Arts Therapies and Psychological Studies Nijmegen Netherlands University of Amsterdam Research Institute of Child Development and Education Amsterdam Netherlands Treatment Centre for People with Mild Intellectual Disabilities and Psychiatric and Behavioral Disorders Gennep Netherlands Information Systems Technology and Design Singapore University of Technology and Design Singapore Music and Machine Learning Lab Music Technology Group Universitat Pompeu Fabra Barcelona Spain Faculty of EEMCS Interactive Intelligence Group Delft University of Technology Delft Netherlands Centre for Digital Music School of Electronic Engineerin

The fields of music, health, and technology have seen significant interactions in recent years in developing music technology for health care and well-being. In an effort to strengthen the collaboration between the involved disciplines, the workshop “Music, Computing, and Health” was held to discuss best practices and state-of-the-art at the intersection of these areas with researchers from music psychology and neuroscience, music therapy, music information retrieval, music technology, medical technology (medtech), and robotics. Following the discussions at the workshop, this article provides an overview of the different methods of the involved disciplines and their potential contributions to developing music technology for health and well-being. Furthermore, the article summarizes the state of the art in music technology that can be applied in various health scenarios and provides a perspective on challenges and opportunities for developing music technology that (1) supports person-centered care and evidence-based treatments, and (2) contributes to developing standardized, large-scale research on music-based interventions in an interdisciplinary manner. The article provides a resource for those seeking to engage in interdisciplinary research using music-based computational methods to develop technology for health care, and aims to inspire future research directions by evaluating the state of the art with respect to the challenges facing each field. © The Author(s) 2021.

关键词： health care interdisciplinarity MedTech music information retrieval (MIR) music neuroscience Music psychology music technology music therapy well-being

Understanding the role of individual units in a deep neural network

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arXiv 2020年

作者： Bau, David Zhu, Jun-Yan Strobelt, Hendrik Lapedriza, Agata Zhou, Bolei Torralba, Antonio Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Adobe Research Adobe Inc. San JoseCA95110 United States Watson Artificial Intelligence Laboratory CambridgeMA02142 United States Media Lab Massachusetts Institute of Technology CambridgeMA02139 United States Estudis d’Informàtica Multimèdia i Telecomunicació Universitat Oberta de Catalunya Barcelona08018 Spain Department of Information Engineering Chinese University of Hong Kong Shatin Hong Kong

Deep neural networks excel at finding hierarchical representations that solve complex tasks over large data sets. How can we humans understand these learned representations? In this work, we present network dissection, an analytic framework to systematically identify the semantics of individual hidden units within image classification and image generation networks. First, we analyze a convolutional neural network (CNN) trained on scene classification and discover units that match a diverse set of object concepts. We find evidence that the network has learned many object classes that play crucial roles in classifying scene classes. Second, we use a similar analytic method to analyze a generative adversarial network (GAN) model trained to generate scenes. By analyzing changes made when small sets of units are activated or deactivated, we find that objects can be added and removed from the output scenes while adapting to the context. Finally, we apply our analytic framework to understanding adversarial attacks and to semantic image *** Codes 68T07 Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

Probabilistic modelling of gait for robust passive monitoring in daily life

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arXiv 2020年

作者： Raykov, Yordan P. Evers, Luc J.W. Badawy, Reham Bloem, Bastiaan Heskes, Tom M. Meinders, Marjan Claes, Kasper Little, Max A. Aston University School of Engineering and Applied Sciences Department of Mathematics Birmingham United Kingdom Radboud university medical center Donders Institute for Brain Cognition and Behaviour Department of Neurology Center of Expertise for Parkinson & Movement Disorders Nijmegen Netherlands Radboud University Institute for Computing and Information Sciences Nijmegen Netherlands University of Birmingham School of Computer Science Birmingham United Kingdom Nijmegen Netherlands UCB Biopharma Brussels Belgium Media Lab Massachusetts Institute of Technology CambridgeMA United States

Passive monitoring in daily life may provide invaluable insights about a person's health throughout the day. Wearable sensor devices are likely to play a key role in enabling such monitoring in a non-obtrusive fashion. However, sensor data collected in daily life reflects multiple health and behavior-related factors together. This creates the need for structured principled analysis to produce reliable and interpretable predictions that can be used to support clinical diagnosis and treatment. In this work we develop a principled modelling approach for free-living gait (walking) analysis. Gait is a promising target for non-obtrusive monitoring because it is common and indicative of various movement disorders such as Parkinson's disease (PD), yet its analysis has largely been limited to experimentally controlled lab settings. To locate and characterize stationary gait segments in free living using accelerometers, we present an unsupervised statistical framework designed to segment signals into differing gait and non-gait patterns. Our flexible probabilistic framework combines empirical assumptions about gait into a principled graphical model with all of its merits. We demonstrate the approach on a new video-referenced dataset including unscripted daily living activities of 25 PD patients and 25 controls, in and around their own houses. We evaluate our ability to detect gait and predict medication induced fluctuations in PD patients based on modelled gait. Our evaluation includes a comparison between sensors attached at multiple body locations including wrist, ankle, trouser pocket and lower back. Copyright © 2020, The Authors. All rights reserved.

关键词： Wearable sensors

Generalized distributed compressive sensing with security challenges for linearly correlated information sources

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Generalized distributed compressive sensing with security ch...

作者： Park, Jeonghun Hwang, Seunggye Yang, Janghoon Bae, Kitae Ko, Hoon Kim, Dong Ku Department of Electrical and Computer Engineering University of Texas AustinTX United States LG Electronics Anyang Korea Republic of Department of New media Seoul Media Institute of Technology Seoul Korea Republic of College of Information & Communication Engineering Sungkyunkwan University Suwon Korea Republic of School of Electrical and Electronics Engineering Yonsei University Seoul Korea Republic of

Distributed compressive sensing (DCS) usually improves the signal recovery performance of multi-signal ensembles by exploiting both intra- and inter-signal correlation and sparsity structure. However, the existing DCS had proposed for a very limited ensemble of signals that has only single common information. This paper proposes a generalized DCS (GDCS) framework which can improve sparse signal detection performance given arbitrary types of common information, which are classified into full common information and partial common information after overcoming against existing limitation. Specifically, the theoretical bound on the required number of measurements under the GDCS is obtained. We also develop a practical algorithm to obtain benefits using the GDCS. At the end of this paper, it simply summarizes the potential security issues when it gets all sensing information in a sensor network. Finally, numerical results verify that the proposed algorithm reduces the required number of measurements for correlated sparse signal detection compared to the DCS algorithm. This research lays down the basis for efficient distributed signal detection so that it can improve the detection performance or it can detect the signal reliably when the number of signal observations is limited. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Compressed sensing

Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

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arXiv 2021年

作者： Hering, Alessa Hansen, Lasse Mok, Tony C.W. Chung, Albert C.S. Siebert, Hanna Häger, Stephanie Lange, Annkristin Kuckertz, Sven Heldmann, Stefan Shao, Wei Vesal, Sulaiman Rusu, Mirabela Sonn, Geoffrey Estienne, Théo Vakalopoulou, Maria Han, Luyi Huang, Yunzhi Yap, Pew-Thian Brudfors, Mikael Balbastre, Yaël Joutard, Samuel Modat, Marc Lifshitz, Gal Raviv, Dan Lv, Jinxin Li, Qiang Jaouen, Vincent Visvikis, Dimitris Fourcade, Constance Rubeaux, Mathieu Pan, Wentao Xu, Zhe Jian, Bailiang De Benetti, Francesca Wodzinski, Marek Gunnarsson, Niklas Sjölund, Jens Grzech, Daniel Qiu, Huaqi Li, Zeju Thorley, Alexander Duan, Jinming Großbröhmer, Christoph Hoopes, Andrew Reinertsen, Ingerid Xiao, Yiming Landman, Bennett Huo, Yuankai Murphy, Keelin Lessmann, Nikolas van Ginneken, Bram Dalca, Adrian V. Heinrich, Mattias P. Institute of Medical Informatics Universität zu Lübeck Ratzeburger Allee 160 Lübeck23562 Germany Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany Department of Radiology and Nuclear Medicine Radboud University Medical Center GA Nijmegen6525 Netherlands The Institute of Medical Informatics Universität zu Lübeck Lübeck23562 Germany The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Fraunhofer MEVIS Institute for Digital Medicine Lübeck23562 Germany The Department of Radiology Stanford University StanfordCA94305 United States The Department of Urology Stanford University StanfordCA94305 United States The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette 91190 France Université Paris-Saclay Institut Gustave Roussy Inserm Radiothérapie Moléculaire et Innovation Thérapeutique Villejuif94800 France The Université Paris-Saclay CentraleSupélec Mathématiques et Informatique pour la Complexité et les Systèmes Inria Saclay Gif-sur-Yvette91190 France The Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen6525 GA Netherlands The School of Automation Nanjing University of Information Science and Technology Nanjing210044 China The School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom The Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital United States Harvard Medical School Boston United States The King’s College London United Kingdom The Tel Aviv University The Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology Hubei Wuhan430074 China MoE Key Laboratory for Biomedical Photonics Collaborative Innovation Center for Biomedical Engineering School of Engineering Sciences Huazhong University of Sci

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://***. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods. © 2021, CC BY.

关键词： Image registration

Work in Progress – Establishing a Master Program in Cyber Physical Systems: Basic Findings and Future Perspectives

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Work in Progress – Establishing a Master Program in Cyber P...

International Conference on Promising Electronic Technologies (ICPET)

作者： Isam Ishaq Rashid Jayousi Salaheddin Odeh Ezzaldeen Edwan Asmaa Shaheen Mohamed Elnaggar Ahmed Elagha Samy Salamah Christian Weber Hasan Abu Rasheed Raimund Kirner Martina Doolan Hamidreza Ahmadian Roman Obermaisser Dhiah el Diehn I. Abou-Tair Ala Khalifeh Sahel Alouneh Zaid Alhalhouli Khalid Alemerien Faiez Gargouri Bassem Bouaziz Nadia Aloui Mohammad Saleh Computer Engineering and Science Departments Al-Quds University Jerusalem Palestine Engineering Program and Computer Department Palestine Technical College - Deir El-Balah Deir El-Balah Palestine Institute of Knowledge Based Systems & Knowledge Management University of Siegen Siegen Germany University of Siegen Siegen Germany School of Engineering and Computer Science University of Hertfordshire Hatfield UK Chair of Embedded Systems University of Siegen Siegen Germany German Jordanian University Amman Jordan School of Electrical Engineering and Information Technology German Jordanian University Amman Jordan Computer and Information Technology Department Tafila Technical University Tafila Jordan Higher Institute of Computer Science and Multimedia University of Sfax Sfax Tunisia Higher Institute of Applied Sciences and Technology of Mateur University of Carthage Mateure Tunisia KTH Royal Institute of Technology Stockholm Sweden

This paper reports on the basic findings and future perspectives of a capacity building project funded by the European Union. The International Master of Science on Cyber Physical Systems (MS@CPS) is a collaborative project that aims to establish a master program in cyber physical systems (CPS). A consortium composed of nine partners proposed the project. Three partners are European and from Germany, UK and Sweden; while the other six partners are from the South Mediterranean region and include: Palestine, Jordan and Tunisia. The consortium is led by the University of Siegen in Germany who also manages the implementation of the work packages. CPS is an emerging engineering subject with significant economic and societal implications, which motivated the consortium to propose the establishment of a master program to offer educational and training opportunities at graduate level in the fields of CPS. In this paper, CPS as a field of study is presented with an emphasis on its importance, especially with regard to meeting local needs. A brief description of the project is presented in conjunction with the methodology for developing the courses and their learning outcomes.

关键词： computer science Internet of Things Industries Knowledge engineering Embedded systems Europe Collaboration

A smart service model in greenhouse environment using event-based security based on wireless sensor network

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A smart service model in greenhouse environment using event-...

作者： Sivamani, Saraswathi Choi, Jongsun Bae, Kitae Ko, Hoon Cho, Yongyun Department of Information and Communication Engineering Sunchon National University 413 Jungangno SuncheonJeonnam57922 Korea Republic of School of Computer Science and Engineering Soongsil University Seoul Korea Republic of Department of New Media Business Seoul Media Institute of Technology 99 Hwagok-ro 61-gil Gangseo-du Seoul07590 Korea Republic of College of Information and Communication Engineering Sunkyunkwan Univeristy Suwon Korea Republic of

In the smart agricultural environment such as greenhouse or vertical farm, the automation process is performed using the environment sensors to maintain the growth of the crops. Currently, the system rely on the defined rule to perform automation, but the situation can turn catastrophic. With the interruption in the communication, data forging, or eavesdropping, the crops will be rotten and destroyed. Therefore, to maintain the automation without failure, the connection needs to be secure and tampering has to be avoided. In this paper, we discuss the security solution for the wireless sensor networks in the automated agricultural environment without any human intervention. The sink node that collects the sensor values organizes the data and transfer the data to the server, along with the XML encryption and private key mechanism for the communication. The secure protocol is used to make connection using the private key, XML ecryption prevents the system from eavesdropping, and forging. Some of the events discussed in the papers are EVNData, SENSORError, DDoSAttack, and ConnThreat. With the help of the event and the tag information, the data security and confidentiality is increased, for the better automation and crop growth. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Crops