Keynote &Plenary Speakers for ICSP 2018


Prof. Alex Kot Chichung, IEEE Fellow and IES Fellow, Nanyang Technological University, Singapore

Biography: Prof. Kot has been with the Nanyang Technological University (NTU), Singapore since 1991. He headed the Division of Information Engineering at the School of Electrical and Electronic Engineering (EEE) for eight years. The Division’s focuses are on signal processing for image, video, speech and audio. He was the Vice Dean Research and Associate Chair (Research) for the School of EEE for three years, overseeing the research activities for the School with over 200 faculty members. He is currently Professor and the Associate Dean (Graduate Studies) for the College of Engineering (COE) and Director of ROSE Lab [Rapid(Rich) Object SEearch Lab) with Peking University, Tencent and Inspur]. He has published extensively with over 200 technical papers in the areas of signal processing for communication, biometrics recognition, data-hiding, authentication and image forensics for digital media. He has two USA and one Singapore patents granted.

Dr. Kot served as Associate Editor for the IEEE Transactions on Signal Processing from 2000 to 2003, IEEE Transactions on Multimedia from 2008 to now, IEEE Transactions on Circuits and Systems for Video Technology from 2000 to 2005; IEEE Transactions on Circuits and Systems Part II from 2004 to 2006; IEEE Transactions on Circuits and Systems Part I from 2005 to 2007, IEEE Transactions on Image Processing, the Signal Processing Magazine, IEEE Signal Processing Letters, and the Senior Editorial Board of IEEE Journal of Special Topics in Signal Processing. He also served as Guest Editor for the Special Issues for the IEEE Transactions on CSVT and JASP. He was a member of the IEEE Transactions on Multimedia Steering Committee and a member of the IEEE SPS Image and Multi-dimensional DSP and IEEE SPS Information, Forensics and Security Technical Committees. Currently, he is in, the Editorial Board member for the EURASIP Journal of Advanced Signal Processing, and the IEEE Transactions on Information Forensics and Security.

He is a member of the IEEE CAS Visual Signal Processing and Communication Technical Committees. He has served the IEEE in various capacities such as the General Co-Chair for the 2004 IEEE International Conference on Image Processing (ICIP) and area/track chairs for several IEEE flagship conferences. He also served as the IEEE Signal Processing Society Distinguished Lecturer Program Coordinator and the Chapters Chair for IEEE Signal Processing Chapters worldwide. He received the Best Teacher of The Year Award at NTU, the Microsoft MSRA Award and as a co-author for the ICPR2008 Best Biometrics Student Paper Award in Florida, USA, the IWDW2010 Best Paper Award in Seoul, Korea and the ISCAS2010 Finalist for the Best Student Paper Award in Paris, France, the IEEE WIFS Best Student Paper Silver Award, the IEEE ICCT 2011 Best Paper Award and the ICECC 2012 Best Paper Award. He was elected as the IEEE CAS Distinguished Lecturer in 2005. He is now a Vice President in the Signal Processing Society, an IEEE Signal Processing Society Distinguished Lecturer, a Fellow of the Academy of Engineering, Singapore, a Fellow of IEEE and a Fellow of IES.

Speech Title: Fake or Real?

Abstract: With the fast proliferation of digital cameras and other image acquisition devices due to the advancement in digital photography technology, photos from the public may have good news values for making journalist reports. However, one big challenge is how to authenticate the photo contents from the public, which may come from unreliable sources. A large variety of forensics works have been proposed to address various forensic challenges based on different types of tell-tale signs. This talk introduces several techniques for: (1) Accurate detection of image demosaicing regularity as a general type of image forensics features. (2) Identification of various common image source models including digital still cameras, RAW conversion tools and the low-end mobile cameras; (3) Universal detection of a wide range of common image tampering. (4) Tampering detection for blur images. (5) EXIF file tampering or content manipulations, (6) Tempering detection with blur images, and (7) Prevention of the image recapturing threat in spoofing, especially in face spoofing. These techniques help expose common image forgeries, especially those easy-to-make forgeries, which can be hardly seen directly by human eyes. The common theme behind these forensics techniques is through statistical detection of some intrinsic image regularity or tampering anomalies.

Prof. Mehmet Celenk, Ohio University, USA


Mehmet Celenk received a Ph.D. degree from Stevens Institute of Technology in EECS in 1983, where he was the Robert Crook Stanley Graduate Fellow in 1985. He served on the Turkish Army in 1984-85 as a lieutenant and joined OU in 1985, where he is currently a Professor of the School of EECS. He has published 300 articles, received a $600,000 hypercube processor grant, participated in a $450,000 Tubitak Autonomous Vehicle Design and Development grant, and secured $120,000 in funds for visiting scholars’ R&D projects. He has directed 35 M.S./Ph.D. theses/dissertations in the School of EECS. He received the distinguished service award from the Signal School in Ankara in 1984 for his R&D work and launching the Communications Journal. He was the recipient of the 1988 Fritz & Dolores Russ Research Award of the Russ College of ENT of OU, and awarded the OU Avionics Academic Challenge Faculty Fellowship in 1988-92. He has been an active reviewer for numerous professional societies (e.g., IEEE, IEE, IET, SPIE, IS&T, IAPR), journals/transactions, publishers, and funding agencies (e.g., NSF, NYSTAR 2002-07). He has been an associate editor of the IEEE Trans. on SMCA (currently SMC: Systems) since 2005 and of the Electronic Letters of the IET since 2015, and the recipient of the Best Associate Award of the IEEE SMC Society in 2010. He has served on the editorial board of the J. Recent Patents on Signal Proc. since 2008, on the Editorial Board of J. of Biometrics and its Applications since 2014, and on TCM of numerous international conferences. He is a member of IEEE, Eta Kappa Nu, and former member of SPIE, IS&T, ACM, ASEE, OE. He was awarded Certificate of Appreciation by SPIE’s Electronic Imaging J. and Optical Engineering for his review services in 2012-13.

Speech Title: Auotonomus Vehicle Guidence in Heavily Shaded Road Conditions

Abstract: The aim of this paper is to investigate a novel method for detection of road lane markers in conjunction with the determination of positioning of the self-driving vehicle relative to lane markers and road boundaries during travel in inclement weather conditions continues to be of paramount importance. This research considers the detection performance and associated parameters using experimental data that demonstrates the accurate results during various conditions. This work presents an investigation and associated results where road land boundary markers are detected in conjunction with the ability decipher the horizon when the front view of the vehicle’s path is degraded. Degradation of driving scenes can be attributed to such weather conditions as heavy rain, fog, or snow. The detection of lane markers and road boundaries is especially important for roads that exhibit severe curves, aggressive uphill slopes and downhill valleys, We present a model to predict deviations from reference distances associated with roads with such design constraints. To address self-driving objectives a method is proposed based on the Least Mean Square (LMS) optimization and the orthogonality principle. The paper also presents a design methodology of the concepts to address autonomous operation of passenger vehicles with some promising experimental results. Error curves are computed and presented for the actual verses predicted lane markers by integrating salient features of the Principal Component Analysis (PCA) and Gradient Specturm Matching (GSM) methods. Multi IR-sensory based fusion is selected as a test bed for the development of an embedded system for autonomous convoy guidance.


Prof. Xudong Jiang, Nanyang Technological University, Singapore

Biography: Prof. Xudong Jiang received the B.Sc. and M.Sc. degree from the University of Electronic Science and Technology of China, and received the Ph.D. degree from Helmut Schmidt University Hamburg, Germany. From 1986 to 1993, he worked as Lecturer at UESTC where he received two Science and Technology Awards from the Ministry for Electronic Industry of China. He was a recipient of the German Konrad-Adenauer Foundation young scientist scholarship. From 1993 to 1997, he was with Helmut Schmidt University Hamburg, Germany as scientific assistant. From 1998 to 2004, He worked with the Institute for Infocomm Research, A*Star, Singapore, as Senior Research Fellow, Lead Scientist and appointed as the Head of Biometrics Laboratory where he developed an software that achieved the fastest and the second most accurate fingerprint verification in the International Fingerprint Verification Competition (FVC2000). He joined Nanyang Technological University, Singapore as a faculty member in 2003 and served as the Director of the Centre for Information Security from 2005 to 2011. Currently, Dr Jiang is a tenured Association Professor in Nanyang Technological University. Dr Jiang has published over 120 research papers, including 20 papers in top IEEE journals: TPAMI, TIP, TSP and SPM, which are well-cited on Web of Science. He is also an inventor of 7 patents (3 US patents). Dr Jiang is a senior member of IEEE, elected voting member of IFS technical committee of IEEE Signal Processing Society, Associate editor of IEEE Signal Processing Letters and IET Biometrics. He has been serving as General Chair, Technical Program Committee Chair, Keynote Speaker and Session Chair of multiple international conferences. His research interest includes pattern recognition, computer vision, machine learning, image analysis, signal processing, machine learning and biometrics.

Speech Title: Iterative Truncated Arithmetic Mean Filter and Its Properties

Abstract: The arithmetic mean and the order statistical median are two fundamental operations in image processing. They have their own merits and limitations in noise attenuation and image structure preservation. Comparing with the arithmetic operation, data sorting required by the median-based filters is a complex process and is intractable for multivariate data. This talk explores the relation between the two very often used fundamental statistics, namely, the arithmetic mean and the order statistical median. It unveils some simple statistics of a finite data set as the upper bounds of the deviation of the median from the mean. It is desirable to develop a filter having the merits of both the types of filters. The proposed Iterative Truncated arithmetic Mean filter, ITM filter, circumvents the data-sorting process but outputs a result approaching the median. Proper termination of the proposed ITM algorithm enables the filters to own merits of the both mean and median filters and, hence, to outperform both the filters in many image processing applications.


Prof. Mao Kezhi, Nanyang Technological University, Singapore


Dr. Mao Kezhi obtained his BEng, MEng and PhD from Jinan University, Northeastern University, and University of Sheffield in 1989, 1992 and 1998 respectively. He worked as a Lecturer at Northeastern University from 1992 to 1995. He joined School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore in 1998, where he is now a tenured Associate Professor. Dr. Mao has over 20 years of research experience in artificial intelligence, machine learning, big data, image processing, natural language processing, and information fusion. He has published over one hundred research papers in referred international journals and conferences. He has edited 3 books published by Springer.
Besides academic research, Dr. Mao is also active in development and consulting. He has successfully developed and delivered several intelligent systems and software tools to government agencies, hospitals and industries.

Dr. Mao Kezhi serves on Editorial Board of Computational Intelligence and Neuroscience. He has served as Keynote Speaker/Programme Chair/Organizing Committee Member for multiple international conferences. In addition, he has served as a reviewer for multiple international journals.


Speech Title: Situation Awareness Based on Automated Analytics of Online News

Abstract: Perceiving the environment, understanding the situation and projecting the future status is the so called “situation awareness”. Situation awareness requires systematic gathering, fusion and analysis of information from various sources. News agencies, big and small, have tens of thousands of journalists around the world, who publish their first-hand information of incidents online at the first time. Nowadays, online news serves as a major source of information.

In this talk, we will introduce an online news text analytics-based automated situation awareness system. This system is able to detect unusual or unexpected incidents happening in the region or other parts of the world in real time or close to real time. Examples of such incidents are terrorism attack, infectious disease breakout, earthquake, tsunami, volcanic eruption, flooding, and typhoon etc.  This system could help understand the situations, predict the future developments and assess the impacts in order to prevent or mitigate the potential negative impacts.