DICTA 2016 Conference

Keynote Speakers

The DICTA 2016 committee welcomes international keynote speakers: Yanxi Liu (Penn State University), Jocelyn Chanussot (Grenoble Institute of Technology), Andreas Dengel (German Research Center for Artificial Intelligence), Zhi-Hua Zhou (Nanjing University).

Professor Yanxi Liu, Penn State University [homepage]

Are you a human or a robot?

Regularities with varying form and scale pervade our natural and man-made world. From insects to mammals, the ability to sense regular patterns has a neurobiological basis and has been observed in many levels of intelligence and behavior. From Felix Kleinʼs Erlanger program, D’Arcy Thompson’s Growth-and-Form, to the Gestalt principles of perception, much of our understanding of the world is based on the perception and recognition of repeated patterns, generalized by the mathematical concept of symmetry and symmetry groups. Given the ubiquity of symmetry in both the physical and the digital worlds, a computational model for symmetry-based regularity perception is especially pertinent to computer vision, computer graphics, robotics and machine intelligence in general, where an intelligent being (e.g. a robot) seeks to perceive, reason and interact with the chaotic world in the most effective and efficient manner. Surprisingly, we have limited knowledge on how humans perceive regular patterns and little progress has been made in computational models for noisy, albeit near-regular patterns in real data. In this talk, I present parallels as well as differences between machine perception and human perception of visual regularity. I shall report our recent results on understanding human perception of wallpaper patterns using neuroimaging (EEG, fMRI) and crowdsourcing, and our successful attempt at building a symmetry-based Turing test to tell humans and robots apart: a symmetry reCAPTCHA.


Yanxi Liu received her Ph.D. degree in computer science for group theory applications in robotics from University of Massachusetts (Amherst, MA, USA) and her postdoctoral training in robotics fine motion planning at LIFIA/IMAG (France). With an NSF research-education fellowship award, Dr. Liu spent one year at DIMACS (NSF center for Discrete Mathematics and Theoretical Computer Science) before joining the Robotics Institute (RI) of Carnegie Mellon University for ten years. She is currently a full professor in the Computer Science Engineering and Electrical Engineering departments of Penn State University, where she co-directs the Lab for Perception, Action and Cognition (LPAC). From 2013-2014, Yanxi took an 18-month-leave visiting Stanford University (Palo Alto, CA), Google (Mountain View, CA) and Microsoft (Sunnyvale, CA).

Dr. Liu's research interests span a wide range of applications in computer vision, computer graphics, robotics, human perception and computer aided diagnosis in medicine, with two central themes: computational regularity and discriminative subspace learning. In year 2000, Dr. Liu coined the term “Computational Symmetry” in a book-chapter on Symmetry, and is the lead author of a 2010 survey 200 pages) on “Computational Symmetry in Computer Vision and Computer Graphics” (Foundations and Trends® in Computer Graphics and Vision). During CVPR 2011 and CVPR 2013, Dr. Liu led two US NSF and industry funded competitions on “Symmetry Detection from Real World Images” (http://vision.cse.psu.edu/research/symComp13/index.shtml), and is leading a continuous effort in creating and maintaining the widely used CMU/PSU Near Regular Texture Database. She has also chaired the first ICCV 2005 workshop on Computer Vision for Biomedical Image Applications: Current Techniques and Future Trends, and edited the associated book (Springer, 563 pages). In 2012, Dr. Liu is the PI of an US NSF grant and the co-director for an interdisciplinary summer school held in Shanghai (Fudan University) on Vision, Learning and Pattern Recognition (VLPR).

Currently, Dr. Liu is an associate editor for IEEE Transaction of Pattern Analysis and Machine Intelligence (PAMI) and Computer Vision and Image Understanding (CVIU) Journal. Dr. Liu served as an area chair/organizing committee member for CVPR/MICCAI/ACCV, and will serve as a program co-chair for CVPR 2017.

Professor Jocelyn Chanussot, Grenoble Institute of Technology [homepage]

Challenges and opportunities in hyperspectral image analysis

Expanding and refining the concepts of colour and multispectral imaging, hyperspectral sensors record the reflectance information of each point on the ground in hundreds of narrow and contiguous spectral bands. The spectral information is instrumental for the accurate analysis of the physical component present in one scene. But, every rose has its thorns: most of the traditional signal and image processing algorithms fail when confronted to such high dimensional data (each pixel is represented by a vector with several hundreds of dimensions).

In this talk, I will start by a general presentation of the challenges and opportunities offered by hyperspectral imaging systems in a number of applications. I will then explore these issues with a hierarchical approach, briefly illustrating the problem of spectral unmixing and of super-resolution, then moving on to pixel-wise classification (purely spectral classification and then including contextual features). Eventually, I will focus on the extension to hyperspectral data of a very powerful image processing analysis tool: the Binary Partition Tree (BPT), providing a generic hierarchical representation of images. Results and illustrations are presented on various hyperspectral images.


Jocelyn Chanussot (M’04–SM’04–F’12) received the M.Sc. degree in electrical engineering from the Grenoble Institute of Technology (Grenoble INP), Grenoble, France, in 1995, and the Ph.D. degree from the Université de Savoie, Annecy, France, in 1998. In 1999, he was with the Geography Imagery Perception Laboratory for the Delegation Generale de l'Armement (DGA - French National Defense Department). Since 1999, he has been with Grenoble INP, where he was an Assistant Professor from 1999 to 2005, an Associate Professor from 2005 to 2007, and is currently a Professor of signal and image processing. He is conducting his research at the Grenoble Images Speech Signals and Automatics Laboratory (GIPSA-Lab). His research interests include image analysis, multicomponent image processing, nonlinear filtering, and data fusion in remote sensing. He has been a visiting scholar at Stanford University (USA), KTH (Sweden) and NUS (Singapore). Since 2013, he is an Adjunct Professor of the University of Iceland. In 2015-2017, he is a visiting professor at the University of California, Los Angeles (UCLA).

Dr. Chanussot is the founding President of IEEE Geoscience and Remote Sensing French chapter (2007-2010) which received the 2010 IEEE GRS-S Chapter Excellence Award. He was the co-recipient of the NORSIG 2006 Best Student Paper Award, the IEEE GRSS 2011 and 2015 Symposium Best Paper Award, the IEEE GRSS 2012 Transactions Prize Paper Award and the IEEE GRSS 2013 Highest Impact Paper Award. He was a member of the IEEE Geoscience and Remote Sensing Society AdCom (2009-2010), in charge of membership development. He was the General Chair of the first IEEE GRSS Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote sensing (WHISPERS). He was the Chair (2009-2011) and Cochair of the GRS Data Fusion Technical Committee (2005-2008). He was a member of the Machine Learning for Signal Processing Technical Committee of the IEEE Signal Processing Society (2006-2008) and the Program Chair of the IEEE International Workshop on Machine Learning for Signal Processing, (2009). He was an Associate Editor for the IEEE Geoscience and Remote Sensing Letters (2005-2007) and for Pattern Recognition (2006-2008). Since 2007, he is an Associate Editor for the IEEE Transactions on Geoscience and Remote Sensing. He was the Editor-in-Chief of the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2011-2015). In 2013, he was a Guest Editor for the Proceedings of the IEEE and in 2014 a Guest Editor for the IEEE Signal Processing Magazine. He is a Fellow of the IEEE and a member of the Institut Universitaire de France (2012-2017).

Professor Andreas Dengel, German Research Center for Artificial Intelligence [homepage]

Augmenting human mind by gaze-based technologies

In the age of digitalization we are confronted with an overwhelming data and process complexity. As a consequence the cognitive load of knowledge workers is continuously increasing and we are lacking in extensions, which augment our mind helping us to perceive, increasing our understanding, enhancing our problem solving capacity, or complementing our skills. This is not a new phenomenon but recent advances in eye tracking technologies allow to use the eyes both as a source for understanding cognitive states but also as a means for interacting with objects and subjects in the real world. Such new options are of increasing interest for developing "electronic information butlers and amplifiers" that are able to anticipate what may be required in a given context and offer us all relevant information. In this talk I like to address the various aspects of augmenting human mind by gaze-based technologies. I will propose and discuss a bunch of approaches and technologies aiming at a more-rapid and better comprehension of human activities as well as their support by learning from gaze behavior. I will demonstrate experimental results from the office field, from medicine as well as from physics education, and further show some applications in the area of infotainment.


Andreas Dengel is a Managing Scientific Director at the German Research Center for Artificial Intelligence (DFKI GmbH) in Kaiserslautern. In 1993, he became a Professor at the Computer Science Department of the University of Kaiserslautern where he holds the chair “Knowledge-Based Systems” and since 2009 he is appointed Professor at the Department of Computer Science and Information Systems at the Osaka Prefecture University. He received his Diploma in CS from the University of Kaiserslautern and his PhD from the University of Stuttgart. He also worked at IBM, Siemens, and Xerox Parc. Andreas is member of several international advisory boards, chaired major international conferences, and founded several successful start-up companies. Moreover, he is co-editor of international computer science journals and has written or edited 12 books. He is author of more than 350 peer-reviewed scientific publications and supervised more than 200 PhD and master theses. Andreas is a IAPR Fellow and received prominent international awards. His main scientific emphasis is in the areas of Pattern Recognition, Document Understanding, Information Retrieval, Multimedia Mining, Semantic Technologies, and Social Media.

Professor Zhi-Hua Zhou, Nanjing University [homepage]

From AdaBoost to Optimal Margin Distribution Machines

AdaBoost is a famous mainstream ensemble learning approach that has greatly influenced machine learning and related areas. A well-known mystery of Adaboost lies in the phenomenon that it seems resistant to overfitting, which has inspired a lot of theoretical investigations. In this talk, we will briefly introduce the margin theory that has a long history of debating but recently defensed. We will show how the theoretical findings provide inspiration for Optimal margin Distribution Machines (ODM), a promising direction of designing powerful learning algorithms.


Zhi-Hua Zhou is a Professor and Founding Director of the LAMDA Group at Nanjing University. His main research interests are in artificial intelligence, machine learning and data mining. He authored the book "Ensemble Methods: Foundations and Algorithms", and published more than 100 papers in top-tier international journals and conference proceedings. His work have received more than 20,000 citations, with a h-index of 71. He also holds 14 patents and has good experiences in industrial collaborations. He has received various awards, including the National Natural Science Award of China (premium science award in China), the PAKDD Distinguished Contribution Award, the Microsoft Professorship Award, 12 international journal/conference paper/presentation/competition awards, etc. He serves as the Executive Editor-in-Chief of Frontiers of Computer Science, Associate Editor-in-Chief of Science China, and Associate Editor of ACM TIST, IEEE TNNLS, etc. He founded ACML (Asian Conference on Machine Learning) and served as General co-chair of IEEE ICDM 2016, Program co-chair IJCAI 2015 Machine Learning track, etc. He also serves as the Chair of the IEEE CIS Data Mining and Big Data Analytics Technical Committee, the CCF Artificial Intelligence Technical Committee, etc. He is a Fellow of the AAAI, IEEE, IAPR, IET/IEE and CCF, and ACM Distinguished Scientist.