Nikolaos G. Bourbakis, Ph. D.
Director, Information Technology Research Institute
OBR Distinguished Professor of Information Technology
Dr. Bourbakis received his B.S. degree in mathematics from the National University
of Athens, Greece, and his Ph.D. in computer science and computer engineering
from the Department of Computer Engineering & Informatics, University of
Previous academic positions include Associate Director of the Center on Intelligent
Systems, Director of the Image-Video-Vision & Applied AI Research Laboratory,
Professor of Electrical Engineering with joint appointment to the Computer
Science Department at SUNY-Binghamton, Professor and Laboratory Director at
the Technical University of Crete, Greece, and Assistant Professor at George
Dr. Bourbakis’ industrial experience includes service at IBM, CA and
Soft Sight, NY. He is founder and Vice President of AIIS, Inc., NY. He pursues
research in Applied AI, Machine Vision, Bioinformatics/Bioengineering, Information
Security, and Parallel/ Distributed Processing funded by USA and European government
and industry. His research work has been internationally recognized and has
won several prestigious awards.
He is the founder and EIC of the International Journal on AI Tools, the Editor-in-Charge
of a Research Series of Books in AI (WS Publisher), Founder and EIC of the
International Journal on Bioinformatics and Bioengineering, (KAP upcoming),
and the Founder and General Chair of several International IEEE Computer Society
Conferences, Symposia and Workshops. He is an IEEE Fellow, a Distinguished
IEEE Computer Society Speaker, an NSF University Research Programs Evaluator,
an IEEE Computer Society Golden Core Member, and an Official Nominator of the
National Academy of Achievements for Computer Science Programs. He is also
listed in WHO’S WHO in Engineering, in Science, in Education, in Intellectuals,
and in Computer Engineering.
Dr. Bourbakis’ Publications
Koutsougeras, C. and Bourbakis, N, “A 3-D visual inspection, diagnosis
and analysis system of PCBs,” Int. Journal Intelligent Systems Engineering,
Bourbakis, N., and Mortazavi, M., “A VLSI design-synthesis methodology
at the transistor layout level,” VLSI Design Journal, accepted to appear
Bourbakis, N., Koutsougeras, C. and Mertoguno, S., “A knowledge based
image tool for VLSI reverse engineering: the layout version,” IEEE Trans
on SMC, to appear in 2002.
Bourbakis, N., “Emulating human visual perception for measuring differences
in images using an SPN graph approach,” IEEE T-SMC, vol. 32, 2, pp. 191-201,
Bourbakis, N., “Low resolution target tracking and recognition from a
sequence of images,” Int. Journal AIT, vol. 11, 4, 2002.
Sikolski, R. and Bourbakis, N, “A parallel, pipeline image transformations
system,” Int. Journal on Computer & EE, vol. 28, pp. 279-310, 2002.
Goldman, D. and Bourbakis, N., “Well-shaped skeletons & fast computation
of the (3, 4) distance transformation,” SPIE Journal Electronic Imaging,
vol. 11, June 2002.
Mannicam, S. and Bourbakis, N, “Image lossless compression and encryption
using SCAN fractals,” PR Society J. Pattern Recognition, vol. 34, 6,
Yang, Y., Bourbakis, N. and Mertoguno, S., “Design of the Kydon’s
RISC processor,” Journal Microprocessor & Microsystems, vol. 25,
1, pp.1-18, 2001.
Dorst, G. and Bourbakis, N., “A hybrid hardware system for real-time
image lossless compression,” Int. Journal Micro processor and Microsystems,
vol. 25, 1, pp.19-31, 2001.
Zong, L. and Bourbakis, N., “Digital video & digital TV: Comparison
and future directions, Int. J. Real-Time Imaging,” vol. 7, pp. 545-556,
Bourbakis, N, “A fusion based method for 3-D perceived representation
of images,” SPIE Society Optical Engineering Journal, vol. 40, 4, pp.
618-626, April 2001.
Bourbakis, N., “A document processing methodology: separating text from
images,” IFAC, IJEAAI, vol. 14, pp. 35-42, 2001.
Spano, S. and Bourbakis, N., “A fuzzy-like controlled multi-fingered
robotic hand,” Int. Journal IS&R, vol. 30, pp. 209-226, 2001.
Yuan, X., Mogzadeh, A., Goldman, D. and Bourbakis, N., “A fuzzy-like
approach to edge detection in colored images,” IAPR Pattern Analysis
and Applications, vol. 4, 4, pp. 272-282, 2001.
Bourbakis, N., Editor, IEEE Bioinformatics and Bioengineering Proceedings,
IEEE Computer Society Press, 2001, 280 pages.
ITRI Research Projects
Bourbakis, N., Co-PI, “Intelligent Computerized Embroidery Design Automation
for the Textile Insustry,” NSF-STTR, February 2001 – August 2002.
Research proposed here focuses on two distinct problems, the first being the
segmentation of color images. This is a problem that has been widely studied
since machine vision first evolved as a research area. However, the vast majority
of this research has predominantly focused on segmentation as a means of preprocessing
to enhance the performance of subsequent recognition mechanisms. Thus, the
needs of applications such as OCR (optical character recognition) or handwriting
recognition as well as other recognition applications have largely shaped the
requirements and targeted performance of segmentation mechanisms. However,
for the application proposed here, the artwork being scanned is often quite
unique and does not usually conform to specific predefined recognizable objects.
Additionally, since the ultimate goal is to produce an accurate reproduction
of the scanned image, the aesthetic accuracy of the segmentation approach is
very important. The second area of research proposed pertains to the interpretation
of singular locations within thin “stroke-like” regions of an image.
Numerous researchers have studied this problem with a specific focus being
placed on applications that require the interpretation of overlapping handwritten
strokes. Here again, the targeted applications have shaped much of the research
that has occurred on this subject. As a result, research proposed here suggests
a new approach that would provide a more robust solution better suited to the
problem at hand. More specifically, past work has predominantly focused on
using template based feature matching and other heuristic based methods to
interpret parts of a region where multiple regular regions (i.e. strokes) overlap
or intersect. These approaches have tended to generalize much of the context
surrounding the singularity to enhance recognition performance. Unfortunately
this generalization will cause problems when preserving and interpreting the
artistic detail of a singularity, which is paramount.