CVIP Lab Book 2002 2
ABOUT CVIP
VISION:
Providing a better understanding of Human and
Computer Vision Systems
Mission:
The Commuter Vision and Image Processing (CVIP) Laboratory was
established in 1994 at the University of Louisville and is committed to
excellence in research and teaching of computer vision and its application
Established in 1994, the CVIP has two broad focus areas: computer vision and medical imaging.
The laboratory hosts unique and modern hardware for imaging, computing and visualization. This
hardware includes two supper computers from SGI (an 40-CPU ONYX2-R1200 and 24-CPU
ONYX-10000), an ImmersaDesk from Fake-Space/Pyramids Systems, a 3-D scanner from
CyberWare, a robotic arm M6-i from Fanuk, over 20 high-end graphics of workstations, and
various imaging hardware. The laboratory is housed in a modern state-of-the-art research building
and is linked, via a high-speed network, to the university’s medical center.
Among the active research projects at the laboratory are the following:
1. Trinocular active vision, which aims at creating accurate 3-D model of indoor
environments. This research is leading to creation of the UofL CardEye active vision
system, which is our research platform in advanced manufacturing and robotics.
2. Multimodality image fusion, which aims at creating robust target models using
multisensory information.
3. Building a functional model of human brain based on integration of structural information
(from CT and MRI) and functional information (from EEG signals and functional-MRI
scans). The functional brain model is our platform for our brain research in learning,
aging, and disfunctions.
4. Image-guided minimally invasive endscopic surgery which aims at creating a system to
assist the surgeons locate and visualize, in real-time, the endoscope’s tip and field of view
during surgery.
5. Large-scale visualization for modeling and simulations of physical systems, and
applications in visual reality.
6. Building a computer vision-based system for reconstruction of human jaw using intra oral
video images. This research will create the UofL Dental Station, which will have various
capabilities for dental research and practice.
7. Vision-based system for autonomous refueling
8. Vision-based system for autonomous vehicle navigation
9. Theoretical work in image modeling, segmentation, registration and pattern recognition.
CVIP Lab Book 2002 3
ABOUT THE UNIVERSITY OF LOUISVILLE
The University of Louisville is a state supported urban university located in Kentucky’s largest
metropolitan area. It was a municipally supported public institution for many decades prior to
joining the university system in 1970. The university has three campuses. The 177-acre Belknap
Campus is three miles from downtown Louisville and houses seven of the universities eleven
colleges and schools. The Health Science Center is situated in downtown Louisville’s medical
complex and houses the university’s health related programs and the University of Louisville
Hospital. On 243-acre Shelby Campus located in eastern Jefferson County are the National Crime
Prevention Institute and the University Center for Continuing and Professional Education. In
recent years, the university has also offered expanded campus courses at Fort Knox, Kentucky.
ABOUT THE SPEED SCIENTIFIC SCHOOL, COLLEGE OF ENGINEERING
Founded in 1924, the University of Louisville Speed Scientific School is the University’s college
of engineering and applied sciences. Endowments from the James Breckinridge Speed
Foundation, named after an influential Louisville industrialist (1844-1912), started and have
continually supported the Speed School. The School consists of Chemical Engineering, Civil &
Environmental Engineering, Computer Engineering and Computer Science, Electrical and
Computer Engineering, Industrial Engineering and Mechanical Engineering programs.
CVIP Laboratory/Lutz Hall, University of Louisville
CVIP Lab Book 2002 4
I. Grants Abstracts
1. Scene Description from Sequence of Images (sponsored by US Army)
This proposal, funded by the Mounted Battle Manuveurs Laboratory, addresses mainly a problem
of dynamic scene reconstruction. In particular, we address the following problem: can an active
vision system, such as the CardEye, be used to faithfully reconstruct a 3-D description of a
dynamic in-door scene at a near real-time frequency? A solution to this problem will open the
door for a multitude of practical applications along the lines of what have been enumerated above.
Our approach to solve this important and challenging problem lies in the creation of a “virtual 3-
D camera” that can take “3-D reconstruction shots” of the scene at “real-time” shutter rate! The
envisioned “3-D camera” is an active vision system, with proper components, mounted on a
flexible mechanism, such as three-segment robotic arm. This arrangement is connected to a fast
computer and visualization hardware to allow the acquisition and display of the “3-D shots”.To
enable full scene construction (up to 360˚) we propose a novel design, that provides the necessary
degrees of freedom in kinematics and the additional sensors to enable reliable data acquisition
2. Novel Approaches in Perception for Autonomous Mobility (sponsored by US Army)
This project describes a strategy for extended research at the Computer Vision and Image
Processing (CVIP Lab) at the University of Louisville (UofL) in the area of machine perception.
In the past seven years, the CVIP Lab has been heavily involved in various aspects of computer
vision research. The laboratory has been focusing on active vision systems in the past four years
and has created an active vision research platform, CardEye, that lends itself to experiments and
hypothesis testing in human and machine perception. The strategy developed in this proposal
builds on the research proposed to the Department of Defense by the CVIP Lab, separately or in
conjunction with other entities such as the Mounted Maneuver Battle Laboratory, Fort Knox,
Kentucky, and Science & Engineering Systems Incorporated (SESI), Radcliff, Kentucky. The
strategy focuses on taking the current capabilities at the CVIP Lab into new frontiers in the area
of perception for autonomous mobility and its applications. Our goal is to be able to use the Card
Eye system for real world applications such as complete scene reconstruction, including dynamic
scenes, robot vision, target tracking and navigation. We also envision to use the system as a
building block in the studies of human interaction and virtual reality. The research proposed here
will focus on the following aspects of this ambitious agenda: 1) sensor arrangement and
integration, 2) reconstruction algorithms, 3) biologically-driven approaches for perception, and 4)
applications.
CVIP Lab Book 2002 5
3. Automatic Lung Cancer Detection (sponsored by Jewish Hospital Foundation)
This research aims at developing a fully automatic Computer-Assisted Diagnosis (CAD) system
for lung cancer screening using chest spiral CT scans. One thousand subjects are enrolled in a
chest cancer screening program in Louisville, KY, USA, which aims at quantification of the
effectiveness of low dose spiral CT scans for early diagnosis of lung cancer, and evaluating its
possible impact on improving the mortality rate of cancer patients. This research presents the first
phase of an image analysis system for 3-D reconstruction of the lungs and trachea, detection of
the lung abnormalities, identification/classification of these abnormalities with respect to specific
diagnosis, and distributed visualization of the results over computer networks.
4. Multi Modality Image Fusion for 3-D Model Building with Applications (sponsored by
Air Force Office of Scientific Research)
The recent advances in sensor systems and the increase availability of ancillary data enable one to
extract far more detailed information than ever before. However, this availability of multisensor
data requires the development of effective data analysis techniques, which can utilize the full
potential of the observed data. There is a consensus in the remote sensing community that one
single modality cannot provide the required information to build an accurate 3D digital model of a
terrain area. Therefore, multisensor integration and data fusion techniques have been used
extensively in terrain mapping, classification, and 3D model building. In this project, we aim to
combine/fuse multi-modality data provided from both spaceborne and airborne sensors, in order
to build a 3D digital model for the sensed target area. The current progress in that project includes
implementation of some algorithms for classification of remote sensing data. Also an algorithm
for decision fusion is implemented.
CVIP Lab Book 2002 6
II. Publications
1. Video Reconstructions in Dentistry
Aly A. Farag, and Ahmed H. Eid
To appear
Abstract- The 3-D reconstruction of the human jaw has tremendous applications. It enables many
orthodontics and dental imaging researches to be applied directly to a digital jaw model, not to a
cast, using computer vision and medical imaging tools. This paper presents two practical
techniques for 3-D modeling of the human jaw from a sequence of intra-oral images. The first
technique is based on the shape from shading algorithm and the other one is based on the space
carving (SC) algorithm for shape recovery. Shape from shading (SFS) technique, using
perspective projection and camera calibration, extracts the 3-D information from a sequence of 2-
D images of the jaw. Data fusion of range data and 3-D registration techniques develop the
complete jaw model. The space carving approach is implemented on a sequence of calibrated
images. On the two reconstructions, we fit a mesh model to the data, in order to create a solid 3-D
model. These models lend themselves for various applications in density. We report experimental
results that show successful application of both approaches to building 3-D models of the human
jaw with sub-millimeter accuracy.
2. Statistical Cerebrovascular Segmentation for Phase-Contrast MRA Data
M. Sabry M., Charles B1. Sites, Aly A. Farag , Stephen Hushek, and Thomas Moriarty
To appear
Abstract- More than 700,000 Americans suffer a major cerebrovascular event, usually a stroke
each year. Stroke is the third leading cause of death and the number one cause of disability.
Serious types of vascular diseases such as carotid stenosis, aneurysm, and vascular malformation
may lead to brain stroke unless they are detected at early stage by building an accurate model of
the vascular system from Phase Contrast Magnetic Resonance Angiography, “PCMRA”, which is
the main goal of our research.
In this paper, we present a statistically based segmentation algorithm to extract vascular tree from
PCMRA. Classification is based on the intensity distribution of the tissues in the data volume,
where voxels are classified as either vessels or non-vessels. Our algorithm is adaptive to reduce
the bias field effect in the images by estimating the model parameters for each slice of the data
volume using the Expectation Maximization, “EM” algorithm for finite mixture of densities. A
connectivity filter is designed taking into account the topology of the vascular tree to remove non-
vessel tissues that appear after statistical segmentation in the form of small islands. We also
present a new Maximum Intensity Projection, “MIP” based technique for validating the results.
The MIP is implemented and used to project both the segmented and original data volume at
different angles. Hence, the resultant images from both volumes can be compared side by side,
which facilitates validation process. Finally, the segmented tree is visualized in 3-D using
Visualization Toolkit, “VTK”, which can be viewed on a stereo graphics workstation or in a
virtual reality environment. We tested our approach on 256x256x117 axial slices of 1 mm
thickness. The results are validated by our medical research team and successfully showed small
MRA vessels down to the limit of scanner resolution. Speed wise, the segmentation algorithm
takes 2 min over SGI Onyx2 supercomputer.
CVIP Lab Book 2002 7
3. Automatic Lung Cancer Detection
Ayman El-Baz and Aly Farag,
To appear
Abstract- We present two novel approaches for segmentation of the lung tissues from the
surrounding structures in the chest cavity, and detection of the abnormalities in the lungs. The
segmentation algorithm is hierarchical; it starts with isolating the background from the chest
cavity, then isolating the lungs from the surrounding structures (e.g., ribs, liver, and other organs
that may appear in chest CT scans). Abnormalities in the lungs are detected by analyzing the
segmented lung tissues and extracting the isolated lumps that appear in various connected regions.
3-D reconstructions are also generated for these abnormalities, in order to be used for subsequent
identification/classification steps. Results of these algorithms are shown on 50 subjects, and have
been evaluated vs. the radiologists. The image analysis approach presented in this research has
provided comparable results with respect to the experts. The approach is quite fast, and lends
itself to distributed visualization over computer networks.
4. Classification of Multispectral Data Using Support Vector Machines Approach for
Density Estimation
Aly A. Farag and Refaat M. Mohamed
To appear
Abstract- In this paper, we present an approach for the classification of remote sensing
multispectral data, which exploits the capabilities of the Support Vector Machines (SVM)
approach for density estimation. Extending the support vector machines to estimate
multidimensional densities is explored. We use these estimates in the design and implementation
of Bayes classification of multispectral Landsat data. Density estimation using SVM is compared
with two traditional approaches, the Parzen window and k-NN approaches. Results on synthetic
and real world remote sensing data show that the SVM estimates are more superior to the other
methods in terms of accuracy, robustness and convergence speed.
5. Monocular, Vision Based, Autonomous Refueling System
Aly Farag, Emir Dizdarevic, Ahmed Eid, and Albert Lörincz
Proceedings of International Workshop on Applications of Computer Vision (WACV), Orlando, Florida,
USA. Dec. 2002.
Abstract- This paper describes design and implementation of a vision based platform for
automated refueling tasks. The platform is an autonomous docking system in principle, with the
specific application– refueling of vehicles. The system is based on monochromatic, monocular
vision, and it utilizes very specialized image processing schemes. Image processing consists of
very fast filtering and segmenting algorithms, as well as moment’s computation. A robotic arm
with 6 joints (FANUC M-6i), and a controller unit (R-J3), does the physical work. A serial
interface, with very high-level commands, connects a supercomputing machine and the robot’s
controller. A practical setup would probably be scaled down to a special design robot, and a
single processor controller with special VLSI chips for image processing. Results are very
promising; the robot can identify the cap position, orientation, and height in real time with
acceptable accuracy and reliability.
CVIP Lab Book 2002 8
6. Image Registration in Multispectral Data Sets
Hani Mahdi and Aly A. Farag
IEEE International Conference on Image Processing (ICIP’2002), Rochester, New York, September 22-25,
2002.
Abstract- This paper discusses the matching of two multispectral data sets using the Genetic
Algorithm (GA) as a search technique for the global optimum estimates of the transformation
parameters. It uses the simplest form of the affine transformation, where the scaling and rotation
are ignored, to explain how the GAs, which are used in the registrations’ processes for the
different bands, can cooperate. This cooperation between the GAs improves the speed of the
matching process. The cooperation depends on the exchange of the intermediate results between
the GAs. Therefore, the proposed approach demands the parallel realization of GAs. In addition,
the proposed approach can be considered as a fusion method for the different decisions resulting
form the registrations for the different bands. Landsat 7-Band and 4-Band aerial data set types are
considered. The aerial data set type is used to explain how to the proposed approach speeds the
matching process.
7. Surface Signatures: An Orientation Independent Free-Form Surface Representation
Scheme for the Purpose of Objects Registration and Matching
Sameh M. Yamany, and Aly A. Farag,
IEEE Transactions on Pattern Analysis and Machine Intelligence – Vol. 24, No 8, August 2002.
Abstract- This paper introduces a new free-form surface representation scheme for the purpose of
fast and accurate registration and matching. Accurate registration of surfaces is a common task in
computer vision. The proposed representation scheme captures the surface curvature information
(seen from certain points) and produces images, called ™surface signatures,º at these points.
Matching signatures of different surfaces enables the recovery of the transformation parameters
between these surfaces. We propose using template matching to compare the signature images. To
enable partial matching, another criterion, the overlap ratio is used. This representation scheme
can be used as a global representation of the surface as well as a local one and performs near real-
time registration. We show that the signature representation can be used to recover scaling
transformation as well as matching objects in 3D scenes in the presence of clutter and occlusion.
Applications presented include: free-form object matching, multimodal medical volumes
registration, and dental teeth reconstruction from intraoral images.
8. A Neural Approach to Zoom-lens Camera Calibration from Data with Outliers
Moumen Ahmed and Aly Farag
Imaging, Vision and Computing – Vol. 20, No. 9-10, August 2002.
Abstract- Camera systems with zoom lenses are inherently more useful than those with passive
lenses due to their flexibility and controllability. However, their calibration raises several
challenges. In this paper, we present a neural framework for zoom-lens camera calibration that
can capture complex variations in the camera model parameters across continuous ranges in the
lens control space, while minimizing the calibration error over all the calibration data. To
automate the tedious process of collecting calibration data, the calibration approach should be
prepared to handle possible outliers in the data. We demonstrate how the calibration approach can
be robust and less sensitive to outliers. The validity and performance of our approach are tested
using both synthetic data with outliers, and with real experiments to calibrate Hitachi CCD
cameras with H10 £ 11E Fujinon active lenses.
CVIP Lab Book 2002 9
9. A General-Purpose Platform for 3-D Reconstruction from Sequence of Images
Ahmed H. Eid, Sherif S. Rashad, and Aly A. Farag
Proc. of 5th International Conference on Information Fusion, MD, Vol. 1, pp. 425-413, July 2002.
Abstract- In this paper we propose a vision system for 3-D reconstruction of objects from
sequence of images. The proposed system lends itself for acquisition of calibrated sequence of
images, and concurrently obtain a direct 3-D reconstruction by laser scanning. Several 3-D
reconstructions techniques can be implemented on the system and then be evaluated against the 3-
D scanning generated from a laser scanner. Validation of the reconstructions is made by pair-wise
comparison with the 3-D scanning results. In addition, other validation approaches are suggested
if the ground truth is not available. Preliminary studies with the proposed vision platform show a
good promise for its use in the validation of various 3-D reconstructions.
10. Experiments in Multimodality Image Classification and Data Fusion
Aly A. Farag, Refaat M. Mohamed and Hani Mahdi
Proceedings of 5th International Conference on Information Fusion, Annapolis, MD, Vol. 1, pp. 299-308,
July 2002.
Abstract- In this paper, we report the results of some experiments on image classification and data
fusion of remote sensing images, as part of ongoing efforts at the CVIP to develop a general
strategy for the analysis of multimodality imaging. Statistical and Fuzzy logic approaches have
been employed in these experiments. In all, six different algorithms for image classification, and
an image fusion algorithm have been implemented and evaluated on common data sets. These
algorithms are: 1) Supervised Parametric Bayes Classifier; 2) Non-parametric Bayesian Classifier
using the Parzen density estimate; 3) Maximum a posteriori classification using the knearest
neighbors (k-NN) approach; 4) MAP Estimation using Markov random field modeling; 5) a
Fuzzy logic approach; and 6) a novel discriminate function classifier. The MAP segmentation of
the regions in the image has been implemented using the Iterated Conditional Mode (ICM)
optimization method. This approach provided the best results, in terms of the minimum
probability of error and best reliability. A novel decision fusion algorithm, based on the a priori
class conditional probability, has been applied to the classifiers’ output.
11.Validation of 3-D Reconstruction from Sequence of Images
Ahmed H. Eid, Sherif S. Rashad, and Aly A. Farag
Proceedings of the International Conference on Signal Processing, Pattern Recognition, and Applications
SSPRA 2002, Crete, Greece, pp. 375-380, June 2002.
Abstract- A number of approaches have been proposed in the literature for reconstruction of 3-D
objects from sequence of images. Yet, very few studies have been reported on the
quantification/validation of the accuracy of these reconstructions. In addition, no design has been
reported for a generic vision platform that can allow various modality imaging. The purpose of
this paper is two folds: 1) propose a vision platform that lend itself for acquisition of calibrated
sequence of images, and concurrently obtain a direct 3-D reconstruction by laser scanning; and 2)
develop and implement different approaches for 3-D reconstructions from sequence of images. 3-
D reconstructions will be evaluated against the 3-D scanning generated from a laser scanner.
CVIP Lab Book 2002 10
12. Fast Automatic Method for 3D Volume Segmentation of the Human Cerebrovascular
M. Sabry M., Charles B1. Sites, Aly A. Farag , Stephen Hushek, and Thomas Moriarty
Proc. of the 13th International Conf. on Computer Assisted Radiology and Surgery, (CARS'02), Paris,
France, June, 2002.
Abstract- We present a new method for 3-D volume segmentation of the human cerebrovascular
structures from Magnetic Resonance Angiograms (MRA) and Magnetic Resonance
Ventriculargrams (MRV). A slice through the volume containing large vein or artery structures is
chosen, which becomes the seed location for the segmentation process. A modified 3-D computer
graphics based region-filling algorithm is used to sweep the vascular tree from seed locations in
order to track and label the vascular structure. The labeled-segmented images are extracted and a
3-D model is created using VTK toolkit. The 3-D model can then be viewed on a stereo graphics
capable workstation or in a Virtual Reality environment. We also present a new maximum
intensity projection (MIP) based technique for validating the results. We implemented MIP and
used it to project both the segmented and original volume at different angles. Hence, the resultant
images from both volumes can be compared side by side, which facilitates validation process.
13. A Modified Fuzzy C-Means Algorithm for Bias Field Estimation and Segmentation of
MRI Data
Mohamed N. Ahmed, Sameh M. Yamany, Nevin Mohamed, Aly A. Farag, and Thomas Moriarty
IEEE Transactions on Medical Imaging, Vol. 21, No. 3, pp. 193-199, March 2002.
Abstract- In this paper, we present a novel algorithm for fuzzy segmentation of magnetic
resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic.
MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or
to problems associated with the acquisition sequences. The result is a slowly varying shading
artifact over the image that can produce errors with conventional intensity- based classification.
Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means
(FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel
(voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect
acts as a regularizer and biases the solution toward piecewise-homogeneous labelings. Such
regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental
results on both synthetic images and MR data are given to demonstrate the effectiveness and
efficiency of the proposed algorithm.
CVIP Lab Book 2002 11
III. Lab News
December
Success Story of CVIP Lab on the SGI homepage (http://www.sgi.com/pdfs/3349.pdf)
WACP2002: Emir Dizdarevic attended the IEEE international workshop on applications of
computer vision.
October
Research Louisville: Aly Farag, was awarded the research Louisville first place prize. Ayman
El-Baz, was awarded the research Louisville first place prize.
ASPRS2002: Refaat Mohamed attended the American Society Photogrammetry and Remote
Sensing Mid-south region Fall Meeting on imaging and geospatial information in the 21st
century, Murray, KY, USA.
A visit to Robots Companies: Ahmed Eid and Emir Dizdarevic visited the iRobot and Active
Media companies in Manchester, NH for more information about new robots purchase.
September
ICIP2002: Aly Farag, and Sheif Rashad attended the International Conference on Image
Processing, Rochester, NY, USA.
August
New Research Assistant: Hongjian Shi, ECE Ph.D. Student, joined the CVIP Lab as a
research assistant
A new release of CVIP Lab homepage (Version II) http://www.cvip.uofl.edu
July
SIGGRAPH 2002: Mohamed Sabry, ECE Ph.D. Student and RA at the CVIP Lab, attended
the 2002 SIGGRAPH Conference in St. Antonio Texas, USA.
IF2002: Refaat M. Mohamed, ECE Ph.D. Student and RA at the CVIP Lab, attended the
Information Fusion Conference 2002 in Annapolis, Maryland.
June
A new robot joins the lab: The first robotic vehicle platform for vision research is delivered
from RoboDirect.com
CARS 2002: Dr. Aly Farag attended the 2002 CARS Conference in Paris where he presented
a research paper on MRA segmentation.
Sponsors Visit: A visit by US Army, sponsors of the Robotic projects in CVIP Lab. Live
demos of vision-based projects by the CVIP lab vision group: Ahmed Eid, Sherif Rashad and
Emir Dizdarevic, were presented.
Summer Medical School: Ayman Elbaz, ECE Ph.D. student and RA at the CVIP Lab,
attended the EMBS Medical Imaging Summer School in France
Summer Trainee: Ramsey Ibrahim, Undergraduate Engineering Student, joined the CVIP
Lab as an undergraduate RA
New Research Assistant: Hossam Hassan and Alaa Aly joined the lab as new research
assistants.
May
The SGI Onyx Supercomputer is upgraded: We added another 300Gb to the Old Onyx
supercomputer for more image storage.
March
SGI Excellence in Visualization Award: Mohamed Sabry, ECE Ph.D. student and RA at the
CVIP Lab, won the SGI Award on Visualization and High Performance Computing.
February
Aly Farag is a University Scholar: Dr. Aly Farag was appointed as University Scholar, for
excellence in Scholarly Activities.
Engineer's Days! Refaat M. Mohamed, ECE Ph.D. Student and RA at the CVIP Lab,
achieved the 2nd place in ECE dept. in the E-Days held by Speed School.
New Grant The CVIP Lab proposal "Perception for Autonomous Mobility" was funded by
the US Army
CVIP Lab Book 2002 12
IV. Awards
Aly Farag was appointed as University Scholar: Dr. Farag, Provost Garrison (on the
podium) and Dr. Nancy Martin,Vice Presdient for Research.
Aly Farag, Research Louisville 2002, won the first prize in research for his innovations in
Biomedical Engineering, 2002. Shown, Dr. Farag and this year's winners.
CVIP Lab Book 2002 13
Ayman El-Baz (middle), ECE Ph.D. student and RA at the CVIP Lab, won the first place
award in student competition, Research Louisville 2002, and Dr. Chenoweth, Assistant Vice
President for Research and Chairman of the ECE Department (left) and
Dr. Farag (right).
Mr. Sabry receiving the SGI Award from Dr. Darrel Chenoweth.
CVIP Lab Book 2002 14
V. Staff
Aly A. Farag was educated at Cairo University (B.S. in Electrical
Engineering), Ohio State University (M.S. in Biomedical Engineering),
University of Michigan (M.S. in Bioengineering), and Purdue University
(Ph.D. in Electrical Engineering). Dr. Farag joined the University of
Louisville in August 1990, where he is currently a Professor of Electrical and
Computer Engineering. His research interests are concentrated in the fields
of Computer Vision and Medical Imaging. Dr. Farag is the founder and
director of the Computer Vision and Image Processing Laboratory (CVIP
Lab) at the University of Louisville, which supports a group of over 20
graduate students and postdocs. Dr. Farag's contribution has been mainly in the areas of active
vision system design, volume registration, segmentation and visualization, where he has authored
or co-authored over 80 technical articles in leading journals and international meetings in the fields
of computer vision and medical imaging. Dr. Farag is an associate editor of IEEE Transactions on
Image Processing. He is a regular reviewer for a number of technical journals and to national
agencies including the NSF and the NIH. He is a Senior Member of the IEEE and SME, and a
member of Sigma Xi and Phi Kappa Phi. Dr. Farag is recently awarded a "University Scholar".
Darrel L. Chenoweth is a Professor in and the Chairman of the Electrical
and Computer Engineering Department, and Assistant Vice President for
Research at the University of Louisville. He joined the University of
Louisville in 1970 after completing his Ph.D. at Auburn University. He has
been involved in image processing and pattern recognition research since
1981, which is sponsored by the Naval Air Warfare Center and the Office of
Naval Research. He is an IEEE Fellow.
Chuck Sites is a University of Louisville staff member for the Electrical
and Computer Engineering Department. He received a Bachelor degree in
Physics from the University of Louisville in 1990. He has over fifteen years
of experience in the computer and electronics industry. He manages the
computer systems and networks of the Electrical and Computer Engineering
Department and is the System Administrator and Technical Advisor for the
CVIP Laboratory.
CVIP Lab Book 2002 15
VI. Students
Ahmed Eid was educated Ayman El-Baz received the
at Mansoura University, B.S. in Electrical Engineering
B.Sc. in Electronics with honors from Mansoura
Engineering with honor, University, Egypt in 1997, and
Mansoura University and M.S. from same university in
M.Sc. in Elect. Comm. 2000. He joined the Ph.D.
Engineering from the same Program in the ECE Department
university. He joined in Summer 2001. Mr. Elbaz is
Mansoura University as a aresearch assitant at the CVIP
teaching assistant in 1996. He Lab working on medical imaging
is currentlya teaching assistant at University of analysis of lung cancer. His interests include statistical
Louisville. He has enrolled in the ECE Ph.D program modeling, genetic algorithms.
at UofL since Aug. 2000. His research interests are
concentrated in the field of Computer Vision.
Mohamed Sabry joined Refaat Mohamed received
the lab as a Ph.D. student in his B.S. in Electrical
the Fall of 2001. Currently, Engineering with honors from
he is working in 3D volume Assiut University, Egypt in
cerebrovascular segmentation 1995, and M.S. from the same
from MRA modality. His university in 2001. He joined
research interests include the Ph.D. Program in the ECE
visualization of large-scale Department in Fall 2001. Mr.
medical data sets, Medical Mohamed is a research
Imaging, Pattern Recognition, assistant at the CVIP Lab
and Image Processing. He won in 2002, the working on Remote Sensing data analysis. His
"Excellence in Visualization Award" from Silicon interests include statistical learning systems and
Graphics. multidimensional classification algorithms.
Hossam Hassan joined Alaa El-Din Aly received his
the lab as a Ph.D. student in B.S. in Electrical Engineering
the summer of 2002. with honors from Assiut
University, Egypt in 1996, and
M.S. from the same university in
2000. He joined the Ph.D.
Program in the ECE Department
in Summer 2002.
Emir Dizdarevic is Hongjian Shi joined the lab
studying for his masters. His as a Ph.D. student in the fall of
research interests include 2002. His research interests
robotics and artificial include Image Processing.
intelligence
CVIP Lab Book 2002 16
ECE Chair and CVIP Staff with Research!Louisville 2002 Competition Winners
Front: Dr. Chenoweth, Chuck Sites, Aly Farag, Salwa Farag, Alaa Aly, Hossam Hassan,
and Refaat Mokhtar;
Back: Ahmed Eid, Tamer Khalaf, Sherif Rashad, Ayman Elabz, and Hong Shi.