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Tutorials

Gabriella Sanniti

Gabriella Sanniti di Baja
Istituto di Cibernetica, National Research Council of Italy (CNR), Pozzuoli, Napoli, Italy
http://www.cib.na.cnr.it/utenti.aspx?U=31&T=2&AspxAutoDetectCookieSupport=1

"Discrete methods to analyse and represent 3D digital objects"

Abstract:
Digital images can be analysed by using continuous or discrete methods. In the first case, after a continuous model is built for the digital object, mathematical tools are used to obtain the analytical solution. This has to be mapped into the discrete space for visualization purposes. In the second case, the digital object is directly processed in the discrete space by applying to its elements discrete tools based on neighborhood configurations. The obtained result is directly a discrete solution, i.e., it consists of image elements. In this tutorial we will follow the discrete approach and will present methods to represent and analyse digital objects. The first part of the tutorial will deal with the description of skeletonization methods. In fact, the skeleton of a digital object is generally regarded as one of the most powerful representation systems and has been used in the framework of shape analysis for 2D and 3D objects. The second part of the tutorial will focus on shape analysis done by following the structural approach. According to this approach, an object can be interpreted as constituted by a number of perceptually meaningful parts and its description can be given in terms of the description of the various parts and of their spatial relationships. Thus, object decomposition methods will be illustrated so as to decompose an object into parts characterized by simple shape.

Short Bio
Gabriella Sanniti di Baja received the doctoral degree cum laude in physics from the University of Naples, Italy, in 1973, and the PhD honoris causa from Uppsala University, Sweden, in 2002. Since 1973, she has been working in the field of image processing and pattern recognition at the Institute of Cybernetics "E. Caianiello" of the National Research Council of Italy, Naples, where she is currently the director of research. Her main research interests include 2D and 3D shape representation and analysis. She has published about 200 papers in international journals and conference proceedings. She has been involved in the organization of several international conferences, is reviewer for major computer science journals and is member of the Program Committee of international conferences. She is co-Editor-in-Chief of Pattern Recognition Letters, has been President of the International Association for Pattern Recognition (IAPR) and of the Italian Group of Researchers in Pattern Recognition (GIRPR), is IAPR Fellow and Foreign Member of the Royal Society of Sciences at Uppsala, Sweden.

Jian Pei

Jian Pei
Simon Fraser University, Canada
http://www.cs.sfu.ca/~jpei/

"Mining Uncertain and Probabilistic Data for Big Data Analytics".

Abstract:
Uncertain data is inherent in many important applications, particularly in the context of big data analytics, such as environmental  surveillance, healthcare informatics, customer-relationship management, market analysis, and quantitative economics research. It is almost impossible to avoid modeling and analyzing uncertainty and probability in conquering big data. Analyzing and mining large collections of uncertain data have become an important task and attracted more and more interest from the data mining and industry application communities.  
In this tutorial, carrying big data analytics as the grand background, we will present a systematic yet compact review on mining uncertain and probabilistic data, including motivations and application examples, problems, challenges, fundamental principles, state-of-the-art methods, the interesting open problems and future directions. We will emphasize big data analytics applications, connections among various mining and analytics tasks, fundamental principles, and open problems.
We assume that the audience has the basic concepts of probability and statistics. However, no deep background knowledge about statistics, sampling, probability, or any other mathematical principles is assumed. We will use sufficient examples to explain the ideas and the intuitions.

Short Bio
Jian Pei is a Professor in the School of Computing Science at Simon Fraser University, Canada. He received a Ph.D. degree from the same school in 2002, under Dr. Jiawei Han's supervision. His expertise is on developing effective and efficient data analysis techniques for novel data intensive applications. He is currently interested in developing various techniques of data mining, Web search, information retrieval, data warehousing, online analytical processing, and database systems, as well as their applications in social networks, health-informatics, and business intelligence. His research outcomes have been adopted by industry and popular open source software suites. His research has been extensively supported in part by government agencies and industry partners. Since 2000, he has published one textbook, two monographs and over 190 research papers in refereed journals and conferences, which have been cited by more than 20,000 times. He is the editor-in-chief of the IEEE Transactions of Knowledge and Data Engineering (TKDE), and an associate editor or editorial board member of the major journals in the field of data mining. He is a senior member of ACM and IEEE, and an ACM Distinguished Speaker. He received several prestigious awards.

Fabio Roli

Fabio Roli
Department of Electrical and Electronic Engineering at the University of Cagliari, Italy
http://pralab.diee.unica.it/en/FabioRoli

"Multiple Classifier Systems"

Abstract:
In the field of pattern recognition, fusion of multiple classifiers is currently used for solving difficult recognition tasks and designing high performance systems. From a theoretical viewpoint, fusion of multiple classifiers allows overcoming the well-known limitations of classical approach to design a pattern recognition system that focuses on the search of the best individual classifier. From a practical viewpoint, the concept of multiple classifiers derives naturally from the context and requirements of many applications. This 3 hours tutorial illustrates the basic concepts and motivations of multiple classifier systems and presents the main methods and algorithms for creating and combining classifier ensembles. Some applications of multiple pattern classifiers are discussed, with a particular focus on security applications (e.g., spam filtering and biometric recognition).

Short Bio
Fabio Roli received his M.S. degree, with honours, and Ph.D. degree in Electronic Engineering from the University of Genoa, Italy. He is professor of computer engineering and Director of the research lab on pattern recognition and applications (http://pralab.diee.unica.it). Dr Roli’s research activity is focused on the design of pattern recognition systems and their applications to biometric personal identification, multimedia text categorization, and computer security. On these topics, he has published more than two hundred papers at conferences and on journals. He was a very active organizer of international conferences and workshops, and established the popular workshop series on multiple classifier systems (www.diee.unica.it/mcs). He is a member of the governing boards of the International Association for Pattern Recognition and of the IEEE Systems, Man and Cybernetics Society. He is Fellow of the IEEE, and Fellow of the International Association for Pattern Recognition. Further details are available on his web site (http://pralab.diee.unica.it/en/FabioRoli).

Tieniu Tan

Tieniu Tan
National Laboratory on Pattern Recognition of China
http://www.nlpr.ia.ac.cn/english/irds/People/tnt.html
"Fundamentals of Iris Recognition"

Abstract:
With an increasing emphasis on security, automated personal identification based on biometrics has recently gained extensive attention from both research community and industry. Iris recognition is becoming one of the most active topics in biometrics due to its high reliability for identification. Great progress has been achieved since the concept of automated iris recognition was first proposed in the 80s.
This tutorial will cover the fundamentals and state of the art of iris recognition, including discussions on each step of a complete iris recognition system (from iris sensor design, iris image databases, liveness detection, iris image quality assessment, iris image synthesis, iris region detection and normalization to iris feature representation and matching). Current applications and remaining issues in iris recognition will also be discussed.

Short Bio
Tieniu Tan received his B.Sc. degree in electronic engineering from Xi'an Jiaotong University, China, in 1984, and his MSc and PhD degrees in electronic engineering from Imperial College London, U.K., in 1986 and 1989, respectively. In October 1989, he joined the Computational Vision Group at the Department of Computer Science, The University of Reading, U.K., where he worked as a Research Fellow, Senior Research Fellow and Lecturer. In January 1998, he returned to China to join the National Laboratory of Pattern Recognition (NLPR), Institute of Automation of the Chinese Academy of Sciences (CAS). He was the Director General of the CAS Institute of Automation from 2000-2007, and has been Professor and Director of the NLPR since 1998. He also serves as Deputy Secretary-General of the CAS. He has published more than 400 research papers in refereed international journals and conferences in the areas of image processing, computer vision and pattern recognition, and has authored or edited 11 books. He holds more than 70 patents. His current research interests include biometrics, image and video understanding, and information forensics and security.
Dr Tan is a Fellow of the IEEE and the IAPR (the International Association of Pattern Recognition). He currently serves as President of the IEEE Biometrics Council, First Vice President of the IAPR and Deputy President of the Chinese Association for Artificial Intelligence. He was the Founding Chair of the IAPR Technical Committee on Biometrics, the IAPR/IEEE International Conference on Biometrics (ICB), the IEEE International Workshop on Visual Surveillance, Asian Conference on Pattern Recognition (ACPR) and Chinese Conference on Pattern Recognition (CCPR). He was the Executive Vice President of the Chinese Society of Image and Graphics, Deputy President of the China Computer Federation and the Chinese Automation Association. He has served as chair or program committee member for many major national and international conferences. He is or has served as Associate Editor or member of editorial boards of many leading international journals including IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Information Forensics and Security, IEEE Transactions on Circuits and Systems for Video Technology, Pattern Recognition, Pattern Recognition Letters, Image and Vision Computing, etc. He is Editor-in-Chief of the International Journal of Automation and Computing. He has given invited talks and keynotes at many universities and international conferences, and has received numerous national and international awards and recognitions.

Sponsors

Asociación Cubana de Reconocimiento de Patrones

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Advanced Technologies Application Center

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International Association for Pattern Recognition

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Lecture Note in Computer Science

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Mexican Association for Computer Vision, Neural Computing and Robotics

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Portuguese Association for Pattern Recognition

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Spanish Association for Pattern Recognition and Image Analysis

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Pattern Recognition of the Brazilian Computer Society

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Chilean Association for Pattern Recognition

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Argentine Society for Pattern Recognition

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International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI)

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Intelligent Data Analysis

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Pattern Recognition Letter

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