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Anastasios (Tas) Venetsanopoulos is a Professor of Electrical and Computer Engineering at Ryerson University in Toronto, Ontario and a Professor Emeritus with the Edward S. Rogers Department of Electrical and Computer Engineering at the University of Toronto, Ontario, Canada.

Education

Tas Venetsanopoulos received the Bachelors of Electrical and Mechanical Engineering degree from the National Technical University of Athens (NTUA) Greece, and a M.S., M. Phil., and a Ph.D. in Electrical Engineering from Yale University, New Haven, Connecticut.
[edit] Research interests

Tas Venetsanopoulos' present research interests are the following: Multimedia (image compression, image and video retrieval); Digital Signal/Image Processing (multichannel image processing, nonlinear, adaptive and M-D filtering, knowledge based processing and recognition, 3-D imaging, biomedical applications); Telecommunications; Biometric Research; and Computational Biology.

Research record

Introduction. Professor Anastasios (Tas) Venetsanopoulos has a long career in research, education and university administration. Over four decades, he has established himself in the worldwide telecommunications and signal processing community as an outstanding researcher, scholar, professor and consultant.[1] He has made contributions to telecommunications, signal and image processing, multimedia and biometrics research by authoring and co-authoring many journal papers and books. His pioneering and fundamental research contributions and the writing of numerous graduate level books have opened up new vistas in telecommunications, multidimensional filter theory and design, design of non-linear filters, multimedia neural networks, biometric applications, and computational biology.

His work has been cited in over 7,000 research papers (Google Scholar) and 400 textbooks. He has been a mentor for over 160 graduate students and post doctoral fellows. He has motivated a generation of engineers in North America and around the world for careers in research and teaching in the areas of signal and image processing, multimedia, telecommunications and biometrics.

Telecommunications. His early work dealt with the problem of optimum detection and signal design, for communication over purely random, general, linear, time-varying, very noisy undersea acoustic channels. The general undersea acoustic channel was modeled as a random, time-varying, linear filter, consisting of a set of randomly moving and correlated scatterers, distributed over a time-varying, random surface. His results contributed to the improvement of SONAR systems for undersea communications over fading dispersive channels.

Later publications focused on the issue of image and video compression and made contributions to the area of progressive image transmission (PIT). PIT refers to the coding of still images at increasing levels of precision, the lower being used for rapid image identification. Through PIT, it is possible to expedite such activities as browsing through remote databases. He developed and tested a number of first and second generation morphological pyramidal techniques, which achieved compression ratios of around 100:1 for good quality, lossy still image transmission. Professor Venetsanopoulos contributed to the area of vector quantization for lossy image compression and developed a number of hierarchical coding techniques for still images. Wavelet techniques for still image compression were also considered, as well as fractal-based techniques compressing and coding still images and video sequences. His latest contributions in telecommunications were in the area of mobility management, with the development of cost effective algorithms for mobile terminal location and determination. This area has attracted interest for its applications in emergency communications, location-sensitive browsing and resource allocation.

Signal and image processing. Tas Venetsanopoulos is one of the first Canadian researchers to make contributions to the foundations of two-dimensional and multi-dimensional digital filtering, which are extensively used in image and video processing. His early contributions in these areas received interest and a variety of techniques that led to efficient two-dimensional filter design were based on these early contributions. In the eighties, his interest was focused on the area of nonlinear filters. Nonlinear filters are more complex than linear filters but allow additional flexibility and speed in complex applications. In the area of nonlinear filters he contributed theoretical results, introducing new filter families. The “Nonlinear Order Statistics Filters” were introduced, a special case of which are linear median, order statistics, homomorphic, a-trimmed median, generalized mean, nonlinear mean and fuzzy nonlinear filters. New versions of polynomial filters, such as quadratic filters, were also studied. New morphological filters, which lead to various detection and recognition applications were also designed. Finally, he conducted extensive research in the area of Adaptive filters. Adaptive Order Statistics filters, Adaptive LMS/RLS filters, Adaptive L-filters and Adaptive morphological filter algorithms were developed. These filters are extensively used in numerous biomedical applications, such as radiology, mammography, tomography, financial data processing and remote sensing applications among others.

In the nineties, he contributed to the area of color image processing and analysis, introducing a number of techniques for color image enhancement filtering and analysis. He introduced the so-called vector directional filter family, which operates along the direction of the color vectors. A new class of adaptive nonlinear filters was developed. Fuzzy membership functions based on different distance measures were adopted to determine the weights of new nonlinear, adaptive filters. The new filters encompass different classes of existing nonlinear filters as special cases. For the first time, the color image was treated as a vector field and edge information carried directly by the color vectors is exploited using vector order statistics.

Multimedia signal processing. In 1999 he became the Inaugural Chair of the Bell Canada Multimedia Systems Laboratory at the University of Toronto. Since then he contributed to the area of multimedia data mining and information retrieval by addressing two key technical challenges: a) the problem of similarity determination within the visual data domain, b) interactive learning of user intentions and automatic adjustment of system parameters for improved retrieval accuracy. He has developed still image and video retrieval systems that utilize color content queries. The system implements a new vector-based approach to image retrieval using an angular-based similarity measure. The developed scheme addresses the drawbacks of the histogram techniques, it is flexible, and outperforms established retrieval systems. He has also developed an interactive learning algorithm for resolving ambiguities arising due to the mismatch between machine-representation of images and human context-dependent interpretation of visual content. His proposed solution exploited feedback from the users during the retrieval sessions to adapt their query intentions and improve the accuracy of the retrieved results.

Biometric research. For thousands of years, humans have used visually perceived body characteristics such as face and gait to recognize each other. This remarkable ability of human visual system has led Professor Venetsanopoulos to build automated systems to recognize individuals from digitally captured facial images and gait sequences. Face and gait recognition belong to the field of biometrics, a very active area of research in the computer vision and pattern recognition society, mainly motivated by government and security-related applications. Face and gait are two typical physiological and behavioral biometrics respectively. Venetsanopoulos has contributed to both areas and his research was cited extensively. There are two general approaches: the model-based approach and the appearance-based approach. Appearance-based face recognition approach processes 2-D facial image as 2-D holistic patterns. The whole face region is the raw input to a recognition system and each face image is commonly represented by a high-dimensional vector consisting of the pixel intensity values in the image. Thus, face recognition is transformed to a multivariate statistical pattern recognition problem. Although the embedding is high-dimensional, the natural constraints of the face data indicate that the face vectors lie in a lower-dimensional subspace (manifold). The popular subspace learning is such a method to identify, represent, and parameterize this subspace with some optimality criteria. Similar to appearance-based face recognition, appearance-based gait recognition approach considers gait as a holistic pattern and uses a full-body representation of a human subject as silhouettes or contours. Gait video sequences are naturally three-dimensional objects, formally named tensor objects, and they are very difficult to deal with using traditional vector-based learning algorithms. In order to deal with these tensor objects effectively, Venetsanopoulos and his research team developed a framework of multilinear subspace learning, so that computation and memory demand are reduced, natural structure and correlation in the original data are preserved, and more compact and useful features can be obtained. Model-based gait recognition approach considers a human subject as an articulated object, represented by various body poses. Professor Venetsanopoulos proposed a full-body layered deformable model (LDM) inspired by the manually labeled body-part-level silhouettes. The LDM has a layered structure to model self-occlusion between body parts and it is deformable, so simple limb deformation is taken into consideration. In addition, it also models shoulder swing. The LDM parameters can be recovered from automatically extracted silhouettes and then used for recognition.

Publications and grants

Tas Venetsanopoulos has authored and co-authored 8 books and has contributed chapters to 30 books, published over 830 academic papers in refereed journals and conference proceedings. Venetsanopoulos' best known contributions to electrical engineering are: "Nonlinear Digital Filters: Principles and Applications",[2] "Artificial Neural Networks: Learning Algorithms, Performance Evaluation, and Applications"[3] and "Color Image Processing and Applications".[4]

He has been supported by grants from the Natural Sciences and Engineering Research Council of Canada (NSERC), the Centers of Excellence of the Province of Ontario, the Canadian Space Agency, Spar Aerospace, Ontario Hydro, the Department of Fisheries and Oceans, Canada, the Department of Communications, Canada, and the Province of Ontario.

Career

Professor Venetsanopoulos joined the Department of Electrical and Computer Engineering (ECE) at the University of Toronto in September 1968 as a Lecturer and he was promoted to Assistant Professor in 1970, Associate Professor in 1973, and Professor in 1981. Venetsanopoulos has served as Chair of the Communications Group and Associate Chair of the Department of Electrical Engineering. Between July 1997 – June 2001, he was Associate Chair of the Graduate Studies of the Department of Electrical and Computer Engineering and was Acting Chair during the spring term of 1998-99. In 1999 a Chair in Multimedia was established in the ECE Department, made possible by a donation of $1.25 Million from Bell Canada, matched with $1 Million of university funds. Venetsanopoulos served as Inaugural Chairholder between 1999 and 2005 and two additional Assistant Professors were hired in the same area. During the period 2001–2006, he served as the 12th Dean of the Faculty of Applied Science and Engineering at the University of Toronto.[5]

Venetsanopoulos’ five-year term as the 12th Dean of the University of Toronto Applied Science and Engineering, the largest and most prominent Faculty of Engineering in Canada, was characterized by an ambitious record of achievement. During his tenure, the "Great Minds" campaign of the Faculty reached $124 Million in external donations matched by an equal amount of funds from granting agencies and foundations. There were two major buildings constructed – the Bahen Centre for Information Technology with the Faculty of Arts and Sciences and the Terrance Centre for Cellular and Biomolecular Research with the Faculty of Medicine. A Strategic Plan for 2004-2010 set the direction for Faculty-wide revitalization. The undergraduate curriculum was revised to offer greater flexibility and enrichment. The Office of the Vice-Dean, Research and Graduate Studies was introduced to enhance the research of the Faculty. An exceptional number of citations of professors of the Faculty resulted, while streamlining of administrative units across the Faculty. The Faculty of Engineering increased its focus on teaching and on the quality of the student experience. There was greater multi-disciplinary collaboration.

Professor Venetsanopoulos was on research leave at the Imperial College of Science and Technology, the National Technical University of Athens, the Swiss Federal Institute of Technology, the University of Florence and the Federal University of Rio de Janeiro, and has also served as Adjunct Professor at Concordia University. In April 2009, he was appointed as the Distinguished Guest Professor of the Communications University of China. He has served as lecturer in 138 short courses to industry and continuing education programs and as Consultant to numerous organizations.[6]

On October 1, 2006, Dr. Venetsanopoulos joined Ryerson University as the Founding Vice-President of Research and Innovation. In that position, Venetsanopoulos accepted the oversight of Ryerson’s international activities, research ethics and the Office of Research Services and the Office of Innovation and Commercialization.

In this role, he announced four strategies to transform the Ryerson University, which was a Polytechnic Institution only a few years before. First, provide stimulus and support to increase both the quality and quantity of scholarly research and creative activity with the stated goal of a research enterprise of $24.4 Million by the 2010-11 fiscal year. Second, to facilitate the transfer of the knowledge created to the community, industry and marketplace. Third, to increasingly pursue partnerships and collaborations that support the overall scholarly research and creative activities plan. Fourth, to provide research opportunities to both undergraduate and graduate students throughout the University. Under his leadership, the research trajectory at Ryerson included international competitions for outstanding postdoctoral fellows, a focus on increasing innovation, international and commercial activity, and university support for excellence and ingenuity[7] among graduate and undergraduate students.

On June 30, 2010, Dr. Venetsanopoulos stepped down from the position of Vice-President Research and Innovation at Ryerson and took a one-year administrative leave and subsequently joined the Department of Electrical and Computer Engineering. In the words of Ryerson's President Sheldon Levy, "As the first ever Vice-President, Research and Innovation at Ryerson, Tas brought to the position an immediate credibility and presence based on his own international research record. He advanced research in ways that established the university as active and competitive in Scholarly Research and creative activity. Under his leadership Ryerson has attracted scholars and postdoctoral fellows with unprecedented momentum, and we have made great progress in visibility, perception and objective rankings related to research." "Externally funded research has more than doubled in the past four years, and Ryerson now ranks in the top half of non-medical universities in Canada for research." "Under Tas' leadership the research trajectory at Ryerson has been one of extraordinary growth and success."[8]

Professional service and awards

Tas Venetsanopoulos has served as Chair on numerous boards, councils and technical conference committees of the Institute of Electrical and Electronic Engineers (IEEE). He served as the Chair of the Toronto Section from 1977 to 1979 and the IEEE Central Canada Council from 1980 to 1982. He was President of the Canadian Society for Electrical Engineering and Vice President of the Engineering Institute of Canada from 1983 to 1986. He was a Guest Editor or Associate Editor for several IEEE journals and the Editor of the Canadian Electrical Engineering Journal (1981–1983). He is a member of the IEEE Communications, Circuits and Systems, Computer, and Signal Processing Societies of IEEE, as well as a member of Sigma Xi, the Association for Computer Machinery, the American Society for Engineering Education, the Technical Chamber of Greece, the European Association of Signal Processing, the Association of Professional Engineers of Ontario (APEO) and Greece.

In 1994, Dr. Venetsanopoulos was awarded an Honorary Doctorate from the National University of Technology in Athens, Greece. In 1996, he was awarded the "Excellence in Innovation" Award from the Information Technology Research Centre of Ontario and the Royal Bank of Canada for his work in image processing. Venetsanopoulos was also awarded the "Millennium Medal of IEEE",[9] and the "MacNaughton Medal".[10] In March 2006, he was a joint recipient of the IEEE Transactions on Neural networks Outstanding Paper Award. He is a Fellow of the Engineering Institute of Canada, the IEEE and the Canadian Academy of Engineering. In 2008, A.N. Venetsanopoulos along with Rastislav Lukac, Bogdan Smolka and Konstantinos N. Plataniotis were awarded the "Most Cited Paper Award" by the Journal of Visual Communication and Image Representation for their work in artificial neural networks.[11] In 2010, Dr. Venetsanopoulos was elected as Fellow of the Royal Society of Canada.[12]

References

^ http://www.solarbuildings.ca/en/main_54
^ http://www.magazine.utoronto.ca/feature/u-of-t-fundraising-campaign-where-the-money-went/
^ http://www.ryerson.ca/news/media/General_Public/20060704_st_mr_vpri.html
^ http://www.engineering.utoronto.ca/news/news_742006
^ http://www.ryerson.ca/ryersontoday/stories/20100310_tas.html
^ http://ewh.ieee.org/reg/7/awards/3rd_millennium.php
^ http://www.ieee.ca/awards/2003.php
^ http://www.elsevier.com/framework_products/promis_misc/mcpa_jvc.pdf
^ http://www.rsc-src.ca/documents/RSC_NF_Citations_EN_FINAL_001.pdf

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