Scientific Program

Conference Series Ltd invites all the participants across the globe to attend 3rd Global Summit and Expo on Multimedia & Artificial Intelligence Holiday Inn Lisbon – continental, Lisbon, Portugal.

Day 2 :

Keynote Forum

Heng Ji

Rensselaer Polytechnic Institute, USA

Keynote: Multimedia Knowledge Extraction: Get things right about complex information
Multimedia 2017 International Conference Keynote Speaker Heng Ji photo
Biography:

Heng Ji is Edward P. Hamilton Development Chair Associate Professor in Computer Science Department of Rensselaer Polytechnic Institute. Her research interests focus on Cross-source Information Extraction. She received several major awards including NSF CAREER award, "AI's 10 to Watch" Award by IEEE Intelligent Systems, “Young Scientist” and Future of Computing Council Member by World Economic Forum.

Abstract:

Knowledge extraction and representation have been the common goals for both the text domain and the visual domain. A few significant benchmarking efforts, such as TREC and TRECVID, have also demonstrated important progress in information extraction from data of different modalities. However, none of the media modality research is complete and fully reliable. Systems using text Knowledge Base Population (KBP) tools cover important high-level events, entities, and relations, but they often do not provide the complete details depicting the physical scenes, objects, or activities. Visual recognition systems, despite the recent progress, still suffer from inadequate abilities in extracting high-level semantics comparable to the counterparts from the text part. In this talk, we will present our recent efforts at developing a Scalable, Portable, and Adaptive Multi-media Knowledge Construction Framework which can exploit cross-media knowledge, resource transfer and bootstrapping to dramatically scale up cross-media knowledge extraction processes.  We have developed novel cross-media methods (including a cross-media deep learning model and “Liberal” KBP) to automatically construct multimodal semantic schema for event, improve extraction through inference and conditional detection, and enrich knowledge through cross-media cross-lingual event co-reference and linking.

Keynote Forum

Anton Nijholt

University of Twente, Netherlands

Keynote: Playful Multimedia in Smart and Playable Cities

Time : 10:35-11:05

Multimedia 2017 International Conference Keynote Speaker Anton Nijholt  photo
Biography:

Anton Nijholt received his PhD in computer science from the Vrije Universiteit in Amsterdam. He held positions at various universities, both inside and outside the Netherlands. In 1989 he was appointed full professor at the University of Twente in the Netherlands. His main research interests are human-computer interaction with a focus on playful interfaces, entertainment computing, and humor generation. He edited various books, most recently on playful interfaces, entertainment computing and playable cities. Nijholt acted as program chair and general chair of many large international conferences on affective computing, entertainment computing, virtual agents, and multimodal interaction. He is chief editor of the section Human-Media Interaction of the journals Frontiers in Psychology, Frontiers in Digital Humanities, and Frontiers in ICT. He is co-editor of the Springer Book Series Gaming Media and Social Effects. Since 2015 he is also Global Research Fellow at the Imagineering Institute in Malaysia.

Abstract:

In research on smart cities the emphasis is on the use of sensors that collect information about a city’s inhabitants’ use of resources, their (real-time) behavior, and, using actuators, provide feedback to its citizens or a city’s management, and make changes to the environment that allow for more efficient use of a city’s resources . Management, efficiency and sustainability are keywords. Smartness in smart cities addresses ways to control energy consumption, increase safety, manage real-time traffic and public events, and manage other ways to make cities more efficient.

There is more to city life than efficiency. Sensors and actuators that make a city smart can be used to introduce smart playful and humorous situations, urban games, and other games that are meant to provide playful experiences or playful participation and contribution to urban design and development. Rather than have sensors and actuators to be introduced for making city life and management more efficient, they can as well be introduced to make city life more playful, increasing playfulness and introducing playful experiences during a citizen’s daily activities. We can talk about playful cities and when citizens are given the opportunity to introduce and configure sensor and actuator networks themselves we can also talk about playable cities.

Playable cities allow inhabitants to introduce their own playful applications. They need access to sensors, actuators and microprocessors. Introducing playfulness and humor in smart environments requires knowledge about humor theories. We discuss the theories and make a transition from the usual verbal humor theories to design principles that allow and stimulate the creation of humor in smart environments. We discuss accidental and intentional occurrences of humor and embed them in a framework of humor creation in smart and digitally enhanced physical environments.

Keynote Forum

Richard Jiang

Northumbria University

Keynote: Privacy and Security of Visual Contents in Big Data Era
Multimedia 2017 International Conference Keynote Speaker Richard Jiang photo
Biography:

I am currently a Senior Lecturer in Computer and Information Science at the Univ. of Northumbria, Newcastle. I received a BSc in Electronics from Huazhong Univ. of Science & Tech. in China and a PhD in Computer Science from Queen’s Univ. Belfast, where its computer science was brought up by Turing laureate Sir Tony Hoare since 1960s. After my PhD study, I joined Brunel Univ. in July 2007 as a RA on an EU-FP6 project (RUSHES) on video indexing. Following this I worked as a RA at Loughborough Univ. (TSB project CrimeVis, 03/2009~09/2010), then at Swansea Univ. (EPSRC project on Sports Visualization, 10/2010~09/2011), Univ. of Bath (TSB project on Video Codec, 10/2011~09/2012) and Univ. of Sheffield (EPSRC project BIMPA, 10/2012~04/2013). I joined Univ. of Northumbria as a Lecturer in May 2013.Currently in Northumbria Univ., I am leading a research team of 5 PhDs ( as 1st Supervisor) and 1 Postdoc on Biometrics, Smart Cities, Medical Diagnosis, and Financial Computing in Dept. Computer and Information Science. I authored or co-authored 21 refereed journal papers and 24 conference papers/books/book chapters. I am a Fellow of Higher Education Academy. I served as the publication co-chair of EUVIP 2016 and the leading editor of a Springer book on biometric big data in 2016.

Abstract:

Keynote Forum

Keynote:
Multimedia 2017 International Conference Keynote Speaker    photo
Biography:

Abstract:

We are also accepting proposals for Symposia and Workshops on all tracks.

All proposals must be submitted to multimedia@conferenceseries.net

  • Multimedia applications and services | Multimedia communications and networking | Virtual Reality | Computer Games Design & Development | Visualization & Human Computer Interaction | Audio, Video, Speech & Signal Processing| Multimedia & AI in Healthcare

Session Introduction

Stylianos (Stelios) Asteriadis

University of Maastricht, the Netherlands

Title: Computer Vision and Machine Intelligence to the service of societal needs
Speaker
Biography:

Stylianos Asteriadis, PhD, MSc, is Assistant Professor at the University of Maastricht, the Netherlands. Stylianos Asteriadis received his PhD from the School of Electrical and Computer Eng. of National Technical University of Athens. His research interests lie in the areas of affective computing, visual computing, machine learning, and human activity recognition, while he has published more than 40 journal and international conference papers in the aforementioned fields. Currently, he is the Principal Investigator for the University of Maastricht in two H2020 collaborative projects (H2020-PHC-2015 Health project ‘ICT4Life- Advanced ICT systems and services for integrated care’ and H2020 ICT-20-2015 ‘MaTHiSiS- Technologies for better human learning and teaching’), while he is a reviewer and program committee member for several journals and conferences in his field

Abstract:

Recent advances in the areas of computer vision, artificial intelligence and data analysis are giving rise to a new era in human-machine interaction. A computer (or a machine, in general) should not be seen as a passive box that just processes information but, instead, today it can – and, actually, should - be seen as a device that can act as a companion, an active tutor or a health coach that can interpret our personalized needs, emotions and activities. Coupling human-machine interaction with ambient assisted living and visual computing is now escaping the tight limits of research laboratories and university departments and real products see their way into the market. We discuss about our current research in the use of computer vision and latest advances in machine intelligence to the benefit of societal needs and, in particular, in the areas of education and health. We will present the pipeline and methods used in our research, as well as the bottlenecks met, both from a technical point of view and a functional one. Subsequently, we will give an overview of computer vision techniques and ambient assisted living technologies utilized for human emotion understanding and activity recognition. Results, within the frame of our current work, with the use of ANNs for learning, in an unsupervised manner, personalized patterns in emotion and/or activity understanding, user profiling and system adaptation will be shown and discussed during the talk.

Md. Haidar Sharif

International University of Sarajevo, Bosnia and Herzegovina

Title: How to track unknown number of individual targets in videos?
Speaker
Biography:

Md. Haidar Sharif received BSc in Electronics and Computer Science from the Jahangirnagar University (Bangladesh) in 2001, MSc in Computer Engineering from Duisburg-Essen University (Germany) in 2006, and PhD in Computer Science from the University of Science and Technology of Lille (France) in 2010. From January 2011 to January 2016, he had been working at Gediz University in Izmir (Turkey) as an Assistant Professor. He has been working at International University of Sarajevo (Bosnia and Herzegovina) since April 2016 as an Assistant Professor. He has his expertise in both computer vision and computer architecture.

Abstract:

Target tracking, which aims at detecting the position of a moving object from video sequences, is a challenging research topic in computer vision. Obstacles in tracking targets can grow due to quick target motion, changing appearance patterns of target and scene, non-rigid target structures, dynamic illumination, inter-target and target-to-scene occlusions, and multi-target confusion. As selection of features affects the tracking results, it is eventful to select right features. Feature selection is closely related to the target representation. A target shape can be represented by a primitive geometric shape including rectangle, ellipse, circle, square, triangle, and point [1]. The efforts of tracking targets or objects in videos as efficient as possible are not new [1, 2, 3, 4]. A vast majority of the existing algorithms primarily differ in the way they use image features and model motion, appearance and shape of the target. In this discussion, we will discuss how to track unknown number of individual targets in videos by leveraging a spatiotemporal motion model of the movers. We will address our innovative idea [4] of how to extract the candidate regions, irrespective of movers' number in the scenes, from the silhouetted structures of movers' pixels. Silhouettes of movers obtained on capturing the local spatiotemporal motion patterns of movers, we can generate candidate regions in the candidate frames in a reflex manner. Candidate frame means a frame where a target template would be matched with one of its available movers. Weak candidate regions are deleted with the help of phase-correlation technique, while strong candidate regions are fed into a combined tracking algorithm of Hungarian process and Kalman filter. Since targets are searched in the strong candidate regions only, some well-established concepts (e.g., integral images [5] and brute force) are left out. Henceforth, search process gets super rapid as compared to brute force since comparative runtime reduced from O(n!) to O(n3) with problem size n. Complete trajectories of individual targets in 3D space are resulted in asymptotic runtime of O(n3). Figure 1 shows a sample output of our framework.

Pascal Lorenz

University of Haute-Alsace, France,

Title: Architectures of next generation wireless networks
Speaker
Biography:

Pascal Lorenz (lorenz@ieee.org) received his M.Sc. (1990) and Ph.D. (1994) from the University of Nancy, France. Between 1990 and 1995 he was a research engineer at WorldFIP Europe and at Alcatel-Alsthom. He is a professor at the University of Haute-Alsace, France, since 1995. His research interests include QoS, wireless networks and high-speed networks. He is the author/co-author of 3 books, 3 patents and 200 international publications in refereed journals and conferences. He was Technical Editor of the IEEE Communications Magazine Editorial Board (2000-2006), Chair of Vertical Issues in Communication Systems Technical Committee Cluster (2008-2009), Chair of the Communications Systems Integration and Modeling Technical Committee (2003-2009), Chair of the Communications Software Technical Committee (2008-2010) and Chair of the Technical Committee on Information Infrastructure and Networking (2016-2017). He has served as Co-Program Chair of IEEE WCNC'2012 and ICC'2004, Executive Vice-Chair of ICC'2017, tutorial chair of VTC'2013 Spring and WCNC'2010, track chair of PIMRC'2012, symposium Co-Chair at Globecom 2007-2011, ICC 2008-2010, ICC'2014 and '2016. He has served as Co-Guest Editor for special issues of IEEE Communications Magazine, Networks Magazine, Wireless Communications Magazine, Telecommunications Systems and LNCS. He is associate Editor for International Journal of Communication Systems (IJCS-Wiley), Journal on Security and Communication Networks (SCN-Wiley) and International Journal of Business Data Communications and Networking, Journal of Network and Computer Applications (JNCA-Elsevier).

He is senior member of the IEEE, IARIA fellow and member of many international program committees. He has organized many conferences, chaired several technical sessions and gave tutorials at major international conferences. He was IEEE ComSoc Distinguished Lecturer Tour during 2013-2014.

Abstract:

Level: Survey, research issues

Theme: Quality of Service, Next generation networks, wireless networks

Summary: Emerging Internet Quality of Service (QoS) mechanisms are expected to enable wide spread use of real time services such as VoIP and videoconferencing. The "best effort" Internet delivery cannot be used for the new multimedia applications. New technologies and new standards are necessary to offer Quality of Service (QoS) for these multimedia applications. Therefore new communication architectures integrate mechanisms allowing guaranteed QoS services as well as high rate communications.

The service level agreement with a mobile Internet user is hard to satisfy, since there may not be enough resources available in some parts of the network the mobile user is moving into. The emerging Internet QoS architectures, differentiated services and integrated services, do not consider user mobility. QoS mechanisms enforce a differentiated sharing of bandwidth among services and users. Thus, there must be mechanisms available to identify traffic flows with different QoS parameters, and to make it possible to charge the users based on requested quality. The integration of fixed and mobile wireless access into IP networks presents a cost effective and efficient way to provide seamless end-to-end connectivity and ubiquitous access in a market where the demand for mobile Internet services has grown rapidly and predicted to generate billions of dollars in revenue.

It covers to the issues of QoS provisioning in heterogeneous networks and Internet access over future wireless networks. It discusses the characteristics of the Internet, mobility and QoS provisioning in wireless and mobile IP networks. This tutorial also covers routing, security, baseline architecture of the inter-networking protocols and end to end traffic management issues.

 

Yuansong Qiao

Athlone Institute of Technology, Ireland

Title: Layer dependency aware multi-view video delivery
Speaker
Biography:

Yuansong Qiao (John) is a Science Foundation Ireland funded Investigator working in the Software Research Institute (SRI) at Athlone Institute of Technology. He has over 15 years’ experience in computer networks and multimedia delivery. Currently, he is leading research teams working on two directions: 1) Information Centric Networking performance optimization for video distribution and IoT data processing; 2) Big Data analytics system optimization using Software Defined Networking technologies. He received his Ph.D. in Computer Applied Technology from the Institute of Software, Chinese Academy of Sciences (ISCAS), Beijing, China, in 2007. He completed a BSc and an MSc in Solid Mechanics from Beijing University of Aeronautics and Astronautics (BUAA), China in 1996 and 1999 respectively. After graduation He joined the ISCAS immediately where he held roles as a network administrator, research engineer and team leader in the R&D area of computer network, multimedia communication and network security protocols and products.

Abstract:

Multi-view video refers to a composite video stream generated by simultaneous capture from multiple cameras covering different portions, or views, of a scene. The Joint Video Team (JVT) has developed H.264/Multi-view Video Coding (MVC) to enhance the compression efficiency for multi-view video. Streaming of multiview video demands high bandwidth even after encoding. Any loss during transmission will have effect on the real-time quality of experience (QoE) of the end user due to the prediction structure used in H.264/MVC encoder. We will address the challenges in delivering MVC video and introduce MVC delivery technologies in both the traditional client/server based model and peer-to-peer (P2P) based model. 

In the traditional client/server based streaming scenario, we have investigated the impacts of network fluctuations (e.g. packet losses) on the quality of streamed MVC video. The test results reveal unexpected differences in video quality amongst the streamed views. An MVC interleaving method is proposed to address this problem, which preferentially transmits the Network Abstraction Layer Unit (NALUs) with higher importance levels for decoding pictures. It reduces transmission errors on more important NALUs and hence enhances the streamed quality of different views.

In the P2P delivery scenario, we have investigated the optimisation problem of maximising outbound bandwidth utilisation of the peers in order to reduce bandwidth usage of the servers. The MVC layer dependency creates challenges in video layer sharing amongst the peers. The layers that can be shared between peers are limited by the layer dependency. A Bit-Torrent based layer-dependency-aware MVC video streaming system has been proposed and evaluated.

Jane You

The Hong Kong Polytechnic University, HongKong

Title: Multimedia-based Healthcare
Speaker
Biography:

Jane You is a full-professor in the Department of Computing, The Hong Kong Polytechnic University. She received her BEng. in Electronic Engineering from Xi’an Jiaotong University in 1986 and Ph.D in Computer Science from La Trobe University, Australia in 1992. She was awarded French Foreign Ministry International Postdoctoral Fellowship in 1993 and also obtained the Academic Certificate issued by French Education Ministry in 1994. She was a tenured senior lecturer at Griffith University, Australia before joining the Hong Kong Polytechnic University.  Her research interests include image processing, medical imaging, computer-aided detection/diagnosis, pattern recognition. So far, she has more than 200 research papers published. She is a team member for three US patents. Her recent work on retinal imaging has resulted in one US patent (2015),  a Special Prize and Gold Medal with Jury’s Commendation at the 39th International Exhibition of Inventions of Geneva (April 2011) and the second place in an international competition (SPIE Medical Imaging’2009 Retinopathy Online Challenge (ROC’2009)). She is also an associate editor of Pattern Recognition and other journals. 

Abstract:

The rapid advances in electronic devices, digital imaging, information technology, computer systems and networks in recent years have stimulated the explosive growth of multimedia computing with diverse applications to different areas including medical service and healthcare. Equipped with various multimedia tools, techniques and services, computerized healthcare is emerging as an ever-increasing important multidisciplinary area which offers tremendous opportunities and excellent facilities to doctors, healthcare professionals and other eligible users to enhance performance by fully utilizing the rich health related multimedia data for effective decision making.  Although the current achievements are exciting and the results can be powerful, it remains a challenging task to manage diversity of health related multimedia data on an open heterogeneous landscape (multi-modality, big volume, mobility, time series) efficiently, accurately, reliably and cost-effectively.

 

This talk presents a general multimedia-based framework to tackle the crucial issues on personalized healthcare. The new medical record e-book structure is proposed to facilitate flexible management of high-dimensional medical data on an open heterogeneous landscape. More specifically, our approach is revolved around three key aspects: 1) multimedia-based medical data management in the context of multi-modality, big volume, mobility and time series; 2) feature selection and fusion of high-dimensional medical data analysis and evaluation with quantitative measurement; 3) classification and decision support scheme for convenient, reliable, efficient and cost effective medical services. A prototype of smart mobile healthcare is developed to demonstrate the feasibility and potentials of the new solution which bridges the gap between data management, medical applications and multimedia computing in a robust environment.

Speaker
Biography:

Irfan Mehmood has been involved in IT industry and academia in Pakistan and South Korea for over 6 years. In Sep 2010, he started professional career as an android developer in Talented Earth Organization, http://www.teo-intl.com/, focusing on conducting design and build advanced applications for the Android platform. In 2015, he joined COMSATS institute of information and technology, Pakistan, as Assistant Professor, where he provided additional duties other than teaching such as project coordinator and supervising research activities of BS and MS students. Currently, he is working as an assistant professor in of department Computer Science and Engineering, School of Electronics and Information Engineering, Sejong University. In addition, he is also coordinator of Global Computer Engineering Program, playing an active role in capacity building, improving teaching quality, and enhancing academia. Moreover, he is in strong research collaboration with various international research groups. He has published numerous articles in peer-reviewed international journals and conferences. He is serving as a professional reviewer for various reputed journals and conferences.

Abstract:

In recent years, there has been a tremendous increase in video capturing devices, which led to large personal and corporate digital video archives. This huge volume of video data became a source of inspiration for the development of vast numbers of applications such as visual surveillance, multimedia recommender systems, and context-aware advertising. The heterogeneity of video data, higher storage, processing cost, and communication requirements demand for a system that can efficiently manage and store huge amount of video data, while providing user-friendly access to stored data at the same time. To address this problem, multimedia summarization schemes have been proposed. Multimedia summarization refers to the extraction of keyframes, identifying most important and pertinent content. In various applications, video summarization can be conducted from the perspective of information prioritization, ranking chosen keyframes relative to their ability to describe the content of the video. A good video summary improves the effectiveness and efficiency of video archiving, cataloging, indexing, as well as increasing the usability of stored videos.

 In this talk, video summarization in general and specifically in the context of prioritization (VSP) will be discussed. Varieties of algorithms, ranging from resource-conscious summarization framework to visual attention-based summarization methods are proposed. Four different VSP techniques are also proposed. The first summarization method is based on a light-weight visual attention model to efficiently extract diagnostically relevant keyframes from wireless capsule endoscopy videos. The second scheme proposes a resource-conscious summarization framework to manage remote sensing wireless capsule video data. The summarization is based on redundancy removal and classification of non-redundant frames into informative and non-informative frames. This framework utilizes cloud resources by adaptively offloading summarization tasks from mobile to cloud. The third and fourth proposed methods explore summarization in the context of prioritization in two different domains: 1) prioritization of brain magnetic resonance images; and 2) saliency-directed prioritization of visual data in wireless surveillance networks. 

Speaker
Biography:

Masahiro Suzuki received his B.A., M.A., and Ph.D. degrees in psychology from Chukyo University in Nagoya, Aichi, Japan in 1994, 1996, and 2002, respectively. He joined the Imaging Science and Engineering Laboratory of Tokyo Institute of Technology in Yokohama, Kanagawa, Japan in 2003 as a postdoctoral researcher. He then joined the Human Media Research Center of Kanagawa Institute of Technology in Atsugi, Kanagawa, Japan in 2006 as a postdoctoral researcher. He will join the Department of Psychology of Tokiwa University in Mito, Ibaraki, Japan in April 2017 as an assistant professor. He is currently engaged in research on 3-D displays and augmented reality. Dr. Suzuki is a member of Japan Society of Kansei Engineering, Japanese Cognitive Science Society, Japanese Psychological Association, Optical Society of Japan, and Vision Society of Japan.

Abstract:

We proposed a technique of obtaining the visually perceived positions of virtual objects presented in front of the screens of 3-D displays, and evaluated it. Applications where users’ own bodies, which are actually seen by users unlike video captured images, interact with virtual objects are attractive applications of 3-D displays. Users expect interactions to be executed when their bodies are seen at the same positions of virtual objects because it is natural for them. Executing interactions when users’ bodies are at the visually perceived positions of virtual objects is the crucial requirement to interactions between the bodies and objects. Conventional techniques execute interaction when users’ bodies are at the positions calculated from binocular disparity of virtual objects. However, the visually perceived positions often differ from the positions calculated from binocular disparity, so that conventional techniques make it difficult to meet the requirement. In contrast to conventional techniques, the proposed technique can meet the requirement by obtaining the visually perceived positions of virtual objects from body movements. According to previous studies on body movements, the velocity of reaching movements as a function of time follows a bell curve. In the proposed technique, the velocity of reaching movements when users reach out to virtual objects is first fitted into a Gaussian function. The final positions of reaching movements are then obtained based on the fitted functions before the movements are finished because virtual objects are seen there. Therefore, the requirement is fulfilled by executing interactions when users’ bodies are at the positions obtained in last step. In the evaluation, we demonstrated the feasibility of the proposed technique by examining the accuracy and precision of the positions obtained with the proposed technique. We also demonstrated the usefulness of the proposed technique  by examining the exactness of interaction executed with the proposed technique. 

Speaker
Biography:

Tsang-Ling Sheu received the Ph.D. degree in computer engineering from the Department of Electrical and Computer Engineering, Penn State University, University Park, Pennsylvania, USA, in 1989. From Sept. 1989 to July 1995, he worked with IBM Corporation at Research Triangle Park, North Carolina, USA. In Aug. 1995, he became an associate professor, and was promoted to full professor in Jan. 2006 at the Dept. of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan. His research interests include wireless networks, mobile communications, and multimedia networking. He was the recipient of the 1990 IBM outstanding paper award. Dr. Sheu is a senior member of the IEEE, and the IEEE Communications Society.

Abstract:

This paper presents a dynamic resource blocks (RBs) allocation scheme for multimedia traffic in LTE networks by utilizing automatic repeated request (ARQ) status report, in which an user equipment (UE) reports erroneous packets to an evolved-node base station (eNodeB). From the status report, eNodeB will compute the amount of successfully received packets per unit time for each UE. Therefore, eNodeB can properly allocate RBs which is exactly the requirement of each UE. Moreover, we consider three different multimedia traffic types (audio, video, and data) with different priorities. Our proposed scheme can alter the modulation determined by automatic modulation and coding (AMC) scheme such that the utilization of an orthogonal frequency division multiplexing access (OFDMA) frame can be substantially increased. To prevent the starvation of data traffic which has the lowest priority, we set an upper bound of employed RBs for audio and video traffic. At last, we perform NS-3 simulation to demonstrate the superiority of the proposed scheme. The simulation results show that our proposed scheme can perform much better than the traditional AMC scheme in terms of RBs utilization, blocking rate of UEs, and the number of successfully connected UEs. Particularly, when in a high noise environment and a large number of UE under an eNodeB, the proposed scheme can achieve relatively smaller blocking rates for QoS-guaranteed multimedia traffic, such as video and audio.

 

Speaker
Biography:

Yuxing Mao, he received the B.Sc. and M.Sc. degrees in radio electrical department from Beijing University, China,in 1989 and 1992, respectively. He got the Ph.D. degree in electrical engineering from Chongqing University, China, in 2004. He worked as a visiting scientist in the Center for Pattern Recognition and Machine Intelligence, CENPARMI, Concordia University, Canada, for one year in 2005. He is a senior member of China Society of Image and Graphics.  He is currently a professor in School of Electrical Engineering, Chongqing University. His research interests include image processing and computer vision, and wireless sensor networks. He has published more than 40 papers in these fields

Abstract:

Super-resolution reconstruction (SRR) is an effective means to address the problem of insufficient image resolution in imaging applications. Existing SRR algorithms use well-focused images and ignore the value of defocused images generated by the imaging system during focusing. The starting point of the present study is to treat a defocused image as distribution and accumulation of scene information among different pixels of the detector, as well as a valid observation of the imaged subject; defocused images are the result of blurring a corresponding high resolution (HR) image using a point spread function (PSF) followed by downsampling. From this starting point, we used multiple defocused images to build an observation model for HR images and propose a SRR algorithm to approach the HR images. We have developed an image degradation model by analyzing optical lens imaging, used the particle swarm optimization (PSO) algorithm to estimate the PSF of the HR image, and used compressed sensing (CS) theory to implement SRR based on the non-coherent characteristics of multiple defocused images. Experiments demonstrate that our method can be used to obtain more information about details of a scene and improve the visual effect without adding any hardware facilities, improving the recognition and interpretation of the image subject.

Speaker
Biography:

Kim Yong-ho got his B. A., M. A. from Seoul National University. Mass Communications (1979, 1985) & Ph. D. from University of Wisconsin, Madison, School of Journalism(1991). After years of research fellowship at Korean Broadcasting Commission (1991-1995), he continues his career as Professor at Dongkuk Univ, & Pukyong National Univ. (1995-1997, 1998-present). His research papers were published in Electronic Library (2008), Communication Research (2010), Journal of The American Society for Information Science (2008, 2010), or were presented at such international conference as ICA, AEJMC, and HEF. Several books were published in Korean, one of which was awarded by the Ministry of Culture of the Korean national government in 2005. He has served for several scholarly organizations and worked for a journal of Broadcasting and Communication since 2012, as the Chief Editor. He is recently interested in the election poll censorship and the automatic key-visual extraction for video summarization. 

Abstract:

Theoretical Background: In the linguistics literature on verbal semantic integration, the N400 effect refers to the fact that unexpected words will cause very low level of negative potentials in brainwave measures around 400ms(milli-seconds) after repeated presented settings and the P600 effect refers to the fact the unexpected words will cause very high level of positive potentials around 600ms.

Methods: Research literature on the video summarization indicates importance of the development of an un-obtrusive method of gathering external information of video users (Z. Lu and K. Grauman,, 2013; W. Ren and Y. Zhu,, 2008; A. Porselvi and S. Gunasundari, 2013). The analysis of event-related potentials (ERP) is such a method which extracts only a result of reaction with respect to certain stimuli from the brain waves.

Findings and Implications: We observed greater maximum potentials at the left prefrontal cortex (FP1, t = 6.930, p = 0.013), the left, right, and middle central lobes (C3, t = 4.670, p = 0.039; Cz, t = 9.818, p = 0.004; C4, t = 10.549 , p = 0.003), and the right and middle frontal-central lobes (FC4, t = 7.370, p = 0.011; FCz, t = 6.541, p = 0.016) of brain wave responses to topic-relevant shots. The right parietal and right temporal-parietal lobes (P8, t = 4.483, p =0.043; TP8, t = 5.326, p = 0.028). It is indisputable to further attempt this sort of ERP analysis of the EEG data during continuous viewing session using topic-relevance ratings from still image testing. Still, the surprisingly large effect of N400 and P600 at prefrontal lobe are asking for further refinement in the future experimental design.

Importance: We developed a method to import time code of video presentation to the EEG data with the topic-relevant information from ratings of topic-relevance for still-image captured from visual shots which were included in the videos. SNR(signal-to-noise ratio) of ERP analysis for the visual shots are about 12.2 well fit in the rage of 10-14 as professional consultant recommended for SNR.

HaoWu

Beijing Normal University, China

Title: Image retrieval based on candidate learning instance
Speaker
Biography:

HaoWu received the B.E. degree and Ph.D from Beijing Jiaotong University, Beijing, China, in 2010 and 2015 respectively. From 2013 to 2015, he worked in Lawrence Berkeley National Laboratory as an research associate. Now, he works in Beijing Normal University as an assistant professor.

His current research interests include pattern recognition, image retrieval, image processing, and image recognition .His current research mainly focuses on image recognition.

Abstract:

Supervised retrieval model has been widely used in the field of computer vision and its high-quality result is supported by enough learning instances  .However, in the process of experiments, it’s difficult to offer enough learning instances for each category. Especially for some special categories, the drawback is more obvious. So how to solve the problem has become one challenging problem.

For this problem, we proposed one new model that can use candidate learning instances to replace the learning instances (In this paper, we mainly consider positive instances). On the one hand, the improved spatial pyramid matching function contributes to retrieve candidate learning instances effectively. On the other hand, an optimized SVM model make the most of candidate learning instances to keep the accuracy of retrieval. At last, we did enough groups of experiments using the new model. The experimental results show that our new model not only can reduce the number of learning instances but also can keep the high-quality of retrieval.

 

 

Speaker
Biography:

Hsien-Sheng Hsiao received PhD degrees in information science from the University of National Chiao-Tung University in 1996. Currently, he is a professor with the Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei, Taiwan. His research interests include e-Learning, mobile learning, STEM education, and Augmented Reality/Virtual Reality for education.

Abstract:

This study focused on how to enhance the interactivity and usefulness of augmented reality (AR) by integrating manipulative interactive tools with a real-world environment. A manipulative AR (MAR) system, which included 3D interactive models and manipulative aids, was designed and developed to teach the unit “Understanding Weather” in a natural science course, and to bridge a formal learning environment (i.e. school), non-formal (i.e. at a museum), and informal learning environments (i.e. home). Sixty-four sixth-grade students (12–13 years old) from four classes in Taipei City were enrolled in a seven-week general studies course entitled “Natural and Life Science and Technology”, and they were divided into an experimental group (31 students who used the MAR system) and a control group (33 students who used multimedia teaching resources). After seven weeks of experiments, the results revealed that integrating the MAR system into inquiry-based field study made a greater positive impact on the students' academic achievement and motivation compared to the multimedia teaching resources installed on a tablet PC. Additionally, there were two interesting findings: (1) the MAR system offered effective learning materials relative to the multimedia teaching resources and (2) manipulative aids were an effective learning tool for interactivity and usefulness of AR. Besides, there were two meaningful suggestions associated with designing and developing the AR educational system for future researchers and designers, namely make it easy to use and include manipulative aids.