Roger
S. Gaborski
Rochester
Institute of Technology
102
Lomb Memorial Drive Rochester, New York 14623-5608
Office: Golisano College of
Computing and Information Sciences, Bldg 70, Room 3647
Phone:
(585)-475-4931
Fax: (585)-475-7100
E-mail: rsg@cs.rit.edu
Homepage: www.cs.rit.edu/~rsg
Current Position
Professor, Department of Computer Science
Extended Faculty Member, Center for Imaging Science
Education
|
Ph.D.
|
Electrical Engineering
|
University of Maryland
|
|
M.S.
|
Electrical Engineering
|
SUNY at Buffalo
|
|
B.S.
|
Electrical Engineering
|
SUNY at Buffalo
|
Research
Interests
- Computer Vision and Video Image
Understanding
- Machine Learning and Pattern
Recognition, including neural networks, genetic algorithms, and
statistical pattern recognition
- Biologically Inspired Computational
Methods
Teaching
Experience
4005-759 Biologically Inspired Intelligent Systems
4005-755 Neural Networks and
Machine Learning
4005-757 Computer Vision
4005-759 Advanced Computer Vision
4005-750 Artificial Intelligence
4003-590 Special Topics in AI -
Distributed Agents
4005-891 Graduate Project Seminar
1051-784 Spatial Pattern Recognition
4003-231 Computer Science 1
4003-232 Computer Science 2
4003-351 Introduction to Digital Design
Current Research
Activities
Automatic
Object
Tracking and Interpretation in Video Sequences (AOTIVS)
The AOTIVS project has two major objectives. The
first
objective of this project is to detect, track and classify both salient
and
non-salient objects in video sequences. The second objective is to
model and
interpret the object’s behavioral activities. The objects may include
individuals, small groups of individuals, crowds and vehicles. Modeled
behaviors will be used as a video indexing method. During the
first phase
of the project a stationary camera will be used to collect the video
sequences.
During the second phase networks of collaborating cameras and
non-stationary
cameras will be used.
VENUS – A
System for novelty Detection in Video Streams with Learning
The VENUS (Video Exploitation and Novelty Understanding in Scenes)
framework
is hierarchical in structure and is comprised of both low level
perceptual and
higher level learning and habituation components. As a result of our
everyday
visual interactions and our analytical abilities, we acquire a general
understanding of the environment and are able to distinguish between
consistent
and inconsistent events based on prior knowledge. Any event that does
not fit our
definition of normalcy tends to be recognized and classified as a novel
event.
If such an event occurs often, the event is ignored as a consequence of
habituation and is no longer considered novel. In this activity, we
extend this
theory of habituation to include learning of what is and what is not a
novel
event for a computer vision system. Astronomical observations,
surveillance
systems, ultrasound and cardiac medical imaging are examples of systems
that
generate streams of video data. It is very tedious for a human analyst
to
observe and interpret all this data and find “novel events”. In order
to manage
and eventually utilize the information extracted from video, it is
necessary to
develop a framework that can extract meaningful semantic information
such that
it reflects the description understood by a human analyst. As a simple example, consider a scene in
which cars travel along a road in a right to left direction. The system
will
automatically learn the environment, that is, the movement of cars in a
given
region of the visual field. If a car now enters the scene, but this
time
travels from left to right, the directional movement of the car would
be
considered inconsistent. It is also possible for an object to be
inconsistent
in attributes such as, color, speed, location, etc.
The goal of this
project is to
develop the foundations for a computational learning framework for
novelty
detection, habituation and understanding based on biologically inspired
principles. Such a framework will enable us to gain a better
understanding of
the issues and challenges associated with extracting a semantic
understanding
from unstructured video data.
Goal Directed
Visual Search Based on Color Cues:
Co-operative Effects of Top-Down & Bottom-Up Visual Attention
The goal of this project is to
develop a model of certain functions of the human visual system. Focus
of
attention is a critical mechanism we adopt while observing our
surroundings.
We, as humans, do not perceive every aspect of the surroundings, but
instead focus
on the interesting aspects and ignore the non-interesting ones. Thus,
attention
acts as a gating mechanism to the higher level processing in the visual
cortex,
processing only attended locations of the scene. This prioritizing of
objects
leads us to the concept of saliency based on low-level features of
objects.
These low level properties of objects are extracted by the bottom up
focus of
attention initially described by Kock & Ullman (1985), where a
saliency map
reflects the relative conspicuity of objects from their surroundings.
In this
project we develop multiresolution (multiscale) intensity difference,
color
opponent and orientation features. The intensity difference and color
opponent
features are implemented using receptive features. The orientation
filters are
modeled using oriented Gabor functions. A top-down cognitive process
modulates
the earlier bottom up processing. For instance, given a visual search
task, the
knowledge about the target’s features biases the perception of the
scene.
High-level knowledge about the object’s features may include shape,
color or
motion information. In this project we first investigate a saliency map
based
on bottom up features. In the second phase of the project the low level
saliency map of natural images are modified by top down color cues. A
top-down
system will be implemented using a neural network modeling the working
memory
area of the brain and the bottom up system is implemented using
conspicuity
maps.
Steganography
and Steganalysis
Steganography is the processing of ‘hiding’ information in images.
The goal
of this project is to investigate published steganographic techniques
and
evaluate their effectiveness over a wide range of image conditions. A
second
goal is the development of steganalysis algorithms to detect if
data is
hidden in an image or not.
Professional
Organization
Membership
- ACM (Association for Computing
Machinery)
- Institute of Electrical and Electronic
Engineers - Senior Member
- IEEE Computer Society
- Eta Kappa Nu (Engineering Honor
Society)
- Sigma Xi (Research Honor Society)
Recent
Professional Activities
- AIPR2004 – Executive committee,
Registration Chair
- Eight International Workshop on
Frontiers in Handwriting Recognition ( IWFHR-8, 2002 ) - technical
committee member
- Engineering of Natural and Artificial
Intelligent Systems (ENAIS’2001) – member of international program
committee
- International Congress on
Computational Intelligence ( 1999 ) – member of program committee,
session chair and presented an invited tutorial on Computer Vision
- Western New York Image Processing
Workshop ( 1998 ) – committee member and session chair
- Western New York Image Processing
Workshop ( 1999 ) – committee member and session chair
Consulting
Activities
Recent consulting arrangements in the areas of
computer vision, machine
learning, image understanding and neural networks:
- RECON/OPTICAL
- RIT Research Corporation
- PSC
- Eastman Kodak Company
Publications, Patents,
Academic Grants and Awards
- More than fifty conference
publications ( author/ co-author)
- Twenty four patents issued (
inventor/ co-inventor)
- Faculty Enhancement and Development
Grant (FEAD), “Investigation of Spiking Neurons for Object Detection,”
2002.
- Faculty Enhancement and Development
Grant (FEAD), "People Detection using Color Detection
Algorithms," 1999.
- National Science Foundation Proposal,
“Biometrics”, in collaboration with SUNY at Buffalo, Columbia, CMU and
Michigan State
- National Science Foundation Grant,
"Knowledge Based Document Understanding," in collaboration with Dr.
Srihari, SUNY at Buffalo, June, 1991 - May, 1993.
- National Security Agency Ph.D.
Fellowship, University of Maryland, 1982.
Professional
Employment
History
Graduate
Coordinator, Department of Computer Science
-
Responsible
for the operation of the graduate program in Computer Science
Adjunct Faculty,
Department of Computer Science and Center for Imaging Science (RIT)
- Taught neural network and pattern recognition
courses
Eastman
Kodak Company, Senior Research Associate
Worked directly with the
Director
of New Business Opportunities and reported to the Vice President,
Health
Imaging Division on technical developments.
-
Managed a group of researchers developing medical
imaging
products. Worked with customers to obtain input for product
improvements.
-
Developed image processing algorithms for digital
medical
imaging products. The algorithms included image segmentation, image
understanding,
enhancement, tone scaling and
compression.
-
Develop an image understanding and visualization
system for
medical images. The techniques employed included shape understanding,
data
fusion techniques and a probabilistic reasoning unit
that incorporated
anatomical knowledge.
-
Developed algorithms for computed aided
mammography.
-
Developed cooperative research programs with
University of
Rochester and SUNY at Buffalo.
Recruited and lead a team of researchers from
several
disciplines to develop document understanding and optical character
recognition
algorithms.
-
Responsible for providing direction in document
understanding
issues, including automatic form recognition and optical character
recognition
(machine print, hand print and cursive).
-
Developed cooperative research programs with
research groups
at SUNY at Buffalo and MCC (Microelectronic and Computer Consortium,
Austin, TX
)
Member of a select team whose
purpose was to formulate the research directions in the area of image
understanding for the Eastman Kodak Company
National Security
Agency
- Developed algorithms for speech understanding
- Developed
special purpose signal processing algorithms and hardware
Calspan
- Developed low power instrumentation to
detect oil
pollution in waterways. This work involved utilizing optical sensors
and
analog/digital circuit design
Presentations
and Publications
Teredesai, A., Kanodia, J., Gaborski, R. and Ahmad, M.,
“COMMA: Combing Multimedia Multi-relational Associations,” (The 7th ACM
SIGKDD
Workshop on Multimedia Data Mining, Seattle, WA, USA, August 2004).
Gaborski, R., Vaingankar, A,
Chaoji, V. and Teredesai, A., "VENUS: A
System for Novelty Detection in Video Streams with Learning," (FLAIRS,
May, 2004).
Gaborski, R.,
Vaingankar, A. and Tentler, A., "Detection of Inconsistent Regions in
Video Streams," (SPIE January, 2004).
Vaingankar, A, Chaoji,
V., Gaborski, R. and Teredesai, A., “Cognitively Motivated Habituation
for
novelty Detection in Video”, (NIPS 2003 workshop on Open Challenges in
Cognitive
Vision, December, 2003.
Gaborski, R., Vaingankar, A. and
Cansova, R., "Goal Directed Visual Search Based on Color Cues:
Co-operative Effects of Top-Down & Bottom-Up Visual Attention,"
(ANNIE, 2003, November, 2003)
Brazeau, J., Humphrey, J. and Gaborski, R., "A Biologically-based Model
of a Visual Saliency Model," (IEEE Western NY Workshop, September,
2003)
Tentler, A., Vaingankar, A.,
Gaborski, R. and Teredesai, A.,
"Event Detection in Video Sequences of Natural Scenes," (IEEE Western
NY Workshop, September, 2003)
Keller, J., Gaborski, R., Vaingankar,
A. and Tentler, A. and Tymann, P., "Parallel Simulation of a
Visual
Saliency Model," ((IEEE Western NY Workshop, September, 2003)
Hollinger, D., Anderson, P., Gaborski, R. and Teredesai, A., “Feature
Extraction
and Classification in Machine Printed Korean OCR,” (IEEE Western NY
Workshop,
September, 2003)
Pakin, S.K., Gaborski, R., Barski, L., Foos, D. and Parker,
K. J.,
"A Clustering Approach to Bone and Soft Tissue Segmentation of Digital
Radiographic Images of Extremities,” Journal of Electronic
Imaging,
January, 2003.
Luo, H., Acharya, R. and Gaborski, R., "Knowledge-based Image
Understanding and Classification System for Medical Image
Databases", SPIE International Symposium on Medical Imaging 2002.
Luo, H., Gaborski, R. and Acharya, R., "Knowledge Representation for
Image Content Analysis in Medical Image Databases", SPIE International
Symposium on Medical Imaging 2001.
Pakin, S.K., Gaborski, R., Barski, L., Foos, D. and Parker,
K. J.,
"Segmentation Of Bone And Soft Tissue Regions In Digital Radiographic
Images Of Extremities,” SPIE International Symposium on Medical
Imaging
2001.
Pakin, S.K., Gaborski, R., Barski, L., Foos, D. and Parker,
K. J.,
"An Algorithm for Segmentation of Bone and Soft Tissue in X-ray
Extremity
Images", 2000 Western New York Image Processing Conference Workshop,
Rochester, New York, 2000.
Luo,H., Gaborski, R. and Acharya, R., "Automatic Segmentation of
Lung Regions in Chest Radiographs: A Model Guided Approach", IEEE
International
Conference on Image Processing (ICIP2000), Vancouver, Canada,
2000.
Luo, H., Gaborski, R. and Acharya, R., "Robust Snake Model",
Computer Vision and Pattern Recognition 2000, CVPR2000, Hilton Head
Island,
SC., 2000.
Luo, H., Acharya, R. and Gaborski,R. , " A Knowledge-based Method
for Automatic Segmentation of Lung Regions in Digital Chest
Radiographs",
1999 Western New York Image Processing Conference Workshop, Rochester,
New
York, 1999.
Sun, Y., Gaborski, R. and Acharya, R., "A Practical Approach for
Locating Bone Structures in Radiographic Pelvis Images", 1999
Western New York Image Processing Conference Workshop, Rochester, New
York,
1999.
Pawlicki, T and Gaborski, R., "Identifying Phangeal Regions in Digital
X-ray Images", International congress on Computational
Intelligence
Methods and applications, Rochester, New York, 1999.
Gaborski, R. and Oswalt, E., "Locating People in
Images", International congress on Computational Intelligence
Methods
and applications, Rochester, New York, 1999.
Luo, H., Acharya, R. and Gaborski,R. , " Fully Automatic
Detection of Spine in Chest Radiographs using Fuzzy Logic Approach,
" Soft computing in Biomedicine, Rochester, New York, 1999.
Luo, H., Acharya, R., Gaborski, R., " A New Fully Automatic Approach to
Detect the Spine from X-ray Image", IEEE Western New York Image
Processing
Workshop, Rochester, New York, 1998.
Cook, L.T., Cox, G.G., Insana, M.F., McFadden, M.A., Hall, T.J.,
Gaborski,
R.S., Lure, F.Y.M., "Comparison of a Cathode-Ray-Tube and Film for
Display
of Computed Radiographic Images," Medical Physics , vol. 25, pp.
1132-1138, 1998.
Gaborski, R.S., "A Knowledge Based Approach for Landmark Detection in
Medical Images," 1997 Western New York Image Processing Workshop,
Rochester, New York, 1997.
Cook, L.T., Cox, G.G., Insana, M.F., McFadden, M.A., Hall, T.J.,
Gaborski,
R.S., Lure, F.Y.M. "Contrast-detail analysis of the effect of image
compression on computed tomographic images," , Proceedings of the SPIE:
Image Perception , Vol. 2712, pp. 128-137, 1996.
Lure, F., Pawlicki, T., and Gaborski, R., "Improvement of Detection
Accuracy in Digital Mammography with a Pruned Neural Net Optimized from
Heuristic Decision Rules," ICNN 96, Washington, DC, 1996.
Lure, F., Gaborski, R., and Pawlicki, F., "Application of Neural
Network-based Multistage System for the Detection of Microcalcification
in
Mammogram Images," SPIE Medical Imaging 1996, Newport Beach, CA. 1996.
Lure, F., Jones, P., and Gaborski, R., "Multiresolution
Unsharp Masking Technique for Mammogram
Image Enhancement," SPIE Medical Imaging 1996, Newport Beach, CA. 1996.
Wen, C., Lure, F., and Gaborski,R., "Evaluation of the Diagnostic
Quality of Chest Images Compressed with JPEG and Wavelet Techniques,"
SPIE
Medical Imaging 1996, Newport Beach, CA. 1996
Cook, L., Cox, G., Insana, M., McFadden, M., Hall, T., Gaborski, R.,
and
Lure, F., ?Contrast-detail Analysis of the Effect of Image Compression
on
Electronically Displayed Computed Radiographic and Computer Tomographic
Images,? SPIE Medical Imaging 1996, Newport Beach, CA. 1996
Wen, C. and Gaborski, R., “Image Quality Evaluation of Chest
Radiographs
Compressed with JPEG and Wavelet Algorithms,” Sixth Far West Perception
Conference, Philadelphia, PA. 1995.
Anderson, P., Gaborski, R., Ge, M., Raghavendra, S., and Lung, M-L.,
“Using
Quasirandom Numbers in NN”, Proceedings of the International ICSC
Symposium on
FUZZY LOGIC, Swiss Federal Institute of Technology (ETH), Zurich,
Switzerland,
1995.
Gaborski, R.S. and Lure, F.Y.M., "An Investigation of Artifacts
Resulting From Lossy Wavelet Compression of Digital Mammograms," 1995
AAPM, Works in Progress Poster, Boston, MA., 1995.
Pawlicki, T. and Gaborski, R.S., "Detection of Scatter Reducing Grids
in Scanned Radiographic Images," 1995 AAPM, Works in Progress Poster,
Boston, MA., 1995.
Gaborski, R. and Jang, B., "Enhancement of Computed Radiographic
Images," IEEE Computer-Based Medical Systems, Lubbock, TX, 1995.
Jones, P., Daly, S., Gaborski, and R., Rabbani, M., "Comparative Study
of Wavelet and DCT Decomposition with Equivalent Quantization and
Encoding
Strategies for Medical Images" SPIE Medical Imaging 1995, San Diego,
California, 1995.
Jang, B., Gaborski, R., "Image Enhancement for Computed Radiographic
Images," SPIE Medical Imaging 1995, San Diego, California, 1995.
Anderson, P., Gaborski,R. and Rao, A., " The Polynomial Classification
System," Proceedings of the 47th Annula Conference of Society for
Imaging
Science and Technology, May, 1994, pp 508-510.
Barski, L., Gaborski, R.S. and Anderson, P., "A Neural Network Approach
to the Segmentation of Digital Radiographic Images,?" ANNIE 93
Artificial
Neural Networks in Engineering, St. Louis, MO, 1993
Rao, A., Anderson, P., and Gaborski, R., "A Hardware Polynomial Feature
Net for OCR", IEE Third International Conference on Artificial Neural
Networks, Brighton, UK, 1993.
Gaborski, R., Anderson, P., and Tilley, D., "Genetic Algorithm
Selection for Handwritten Character Identification," International
Conference on Neural Networks and Genetic Algorithms, Innsbruck,
Austria, 1993.
Anderson, P. and Gaborski, R., "The Polynomial Method Augmented by
Supervised training for Hand Printed Character recognition," Innsbruck,
Austria, 1993.
Gaborski, R. and Barski, L., "Improved Readability of Machine Printed
Characters," Advanced Technology Conference, US Postal Service,
Washington, D.C., 1992.
Lee D., Srihari S., Gaborski R. . Baysian and Neural Network Pattern
Recognition: a Theoretical Connection and Empirical Results with
Handwritten
Characters. In: Artificial Neural Networks and Statistical Pattern
Recognition,
1991. Sethi I. K., Jain A. K. (Eds.) 89 - 108. Elsevier Science.
Gaborski, R. and Barski, L., "The Current State of cursive Script
Recognition at Kodak," 87th InterPlant Conference- Electronic and
Hybrid
Imaging Systems, Rochester, NY, 1991.
Barski, L. and Gaborski, R., "Combined Segmentation and optical
Character Recognition Using a Neural Network," 87th InterPlant
Conference-
Electronic and Hybrid Imaging Systems, Rochester, NY, 1991.
Gaborski, R.,"An Intelligent Character Recognition System Based on
Neural Networks," Kodak Research Magazine, Spring, 1990.
Lee, D., Srihari, S., and Gaborski, R., "Experiments in Handwritten
Character Recognition with Pattern Recognition and Neural Network
Approaches," Advanced Technology Conference, US Postal Service,
Washington, D.C., 1990.
Patents
Issued ( inventor or
co-inventor):
US06018590 Technique for Finding the Histogram Region of
Interest
Based on Landmark Detection for Improved Tonescale Reproduction of
Digital
Radiographic Images 01/26/2000
US05995682 Method for Resizing of a Digital Image 10/30/99
US05978518 Image Enhancement in Digital Image Processing 10/02/99
US05943435 Body Part Recognition in Radiographic Images
08/24/99
US05923775 Apparatus and Method for Signal Dependent
Noise
Estimation and Reduction in Digital Images 07/13/1999
US05862249 Automated Method and System for Determination
of
Positional Orientation of Digital Radiographic Images
01/19/1999
US05857030 Automated Method and System for Digital Image
Processing of Radiologic Images Utilizing Artificial Neural
Networks 01/05/1999
US05796862 Apparatus and Method for Identification of
Tissue
Regions in Digital Mammographic Images 08/18/1998
US05696805 Apparatus and Method for Identifying Specific
Bone
Regions in Digital X-ray Images 12/09/1997
US05661818 A Method and System for Detecting Grids in a
Digital
Image 08/26/1997
US05574803 Character Thinning using Emergent Behavior of
Populations
of Competitive Locallly Independent Processes
11/12/1996
US05553162 Method for Detecting Ink Jet or Dot Matrix
Printing 09/03/1996
US05479523 Constructing Classification Weights Matrices for
Pattern
Recognition Systems using Reduced Element Feature
Subsets
12/26/1995
US05442715 Method and Apparatus for Cursive Script
Recognition 08/15/1995
US05426684 Technique for Finding the Histogram Region of
Interest for
Improved Tone Scale Reproduction of Digital Radiographic
Images 06/20/1995
US05392447 Image-based Electronic Pocket Organizer with
Integral
Scanning Unit 02/21/1995
US05299269 Character Segmentation Using an Associative
Memory
for Optical Character Recognition 03/29/1994
US05212741 Pre-processing of Dot-Matrix/Ink Jet Printed
Text for
OCR 05/18/1993
US05054102 Self-centering Character Stroke Thickening
for
Optical Character Recognition 10/01/1991
US05052044 Correlated Masking Process for Deskewing,
Filtering,
and Recognition of Vertically Segmented Characters
09/24/1991
US05052043 Neural Network with Back Propagation
Controlled
through an Output Configdence Measure 09/24/1991
US05048097 Optical Character Recognition Neural Network
System
for Machine-printed Characters 09/10/1991
US04949392 Document Recognition with Automatic Indexing
for
Optical Character Recognition 08/14/1990
US04791679 Image Character Enhancement Using a Stroke
Strengthening Kernel 12/13/1988