University of Florida
Department of Electrical and Computer
EEE 6512, Section 012A
Image Processing and Computer Vision
This is a 3-credit course.
This course introduces fundamental concepts and techniques for image processing and
computer vision. We will address 1) how to efficiently represent and process
image/video signals, and 2) how to deliver image/video signals over networks. Topics
to be covered include: image acquisition and display using digital devices,
properties of human visual perception, sampling and quantization, image enhancement, image restoration,
two-dimensional Fourier transforms, linear and nonlinear filtering,
morphological operations, noise removal, image deblurring, edge detection, image
registration and geometric transformation, image/video compression, video
communication standards, video transport over the Internet and wireless
networks, object recognition and image understanding.
- Rafael C. Gonzalez, Richard E. Woods, ``Digital Image Processing,'' 3rd Edition, Prentice
Hall; ISBN: 013168728X; August 2007.
- Rafael C. Gonzalez, Richard E. Woods, ``Digital Image Processing,'' 2nd Edition, Prentice
Hall; ISBN: 0201180758; January 15, 2002.
- George Siogkas, "Visual
Media Processing Using Matlab Beginner's Guide," Packt
Publishing, 2013. ISBN-10: 1849697205|ISBN-13: 978-1849697200
- Oge Marques, Practical Image and Video Processing Using MATLAB, Wiley,
New York, NY, 2011. ISBN-10: 0470048158 | ISBN-13: 978-0470048153
- Rafael C. Gonzalez, Richard E. Woods,
and S. L. Eddins, ``Digital
Image Processing Using MATLAB,'' Prentice Hall, 2004. ISBN
- Anil K. Jain, ``Fundamentals of digital image
processing,'' Englewood Cliffs, NJ : Prentice Hall, 1989.
- Y. Wang, J. Ostermann, and Y.Q.Zhang, "Video Processing and Communications,"
1st ed., Prentice Hall, 2002. ISBN:
- D. Taubman and M. Marcellin, "JPEG2000: Image Compression Fundamentals,
Standards, and Practice," Kluwer, 2001. ISBN: 079237519X.
- David A. Forsyth, Jean Ponce, "Computer
Vision: A Modern Approach," Prentice Hall; 1st edition (August 14, 2002),
- Richard Hartley, Andrew Zisserman, "Multiple
View Geometry in Computer Vision," Paperback: 672 pages; Publisher:
Cambridge University Press; 2 edition (March 25, 2004) ISBN: 0521540518
- Yi Ma, Stefano Soatto, Jana Kosecka, S.
Shankar Sastry, "An
Invitation to 3-D Vision," Hardcover: 526 pages ; Publisher: Springer-Verlag;
(November 14, 2003) ISBN: 0387008934
- A. Ardeshir Goshtasby, "2-D and 3-D Image
Registration," Wiley Press, April. 2005. [ebook on
- John W. Woods, "Multidimensional Signal, Image, and Video Processing and
Coding," Academic Press; (March 13, 2006), ISBN-10: 0120885166, ISBN-13:
- Linda G. Shapiro and George C. Stockman, "Computer Vision," Prentice-Hall,
Inc., Upper Saddle River, New Jersey, 2001 (ISBN 0-13-030796-3).
- Emanuele Trucco and Alessandro Verri, "Introductory Techniques for 3-D
Computer Vision," Prentice-Hall, Inc., Upper Saddle River, New Jersey, 1998
- Iain E G Richardson, "H.264 and MPEG-4 Video Compression," John Wiley &
Sons, September 2003, ISBN 0-470-84837-5
- M. E. Al-Mualla,
C. N. Canagarajah and D. R. Bull, Video
Coding for Mobile Communications: Efficiency, Complexity and Resilience,
Elsevier Science, Academic Press, 2002. ISBN: 0120530791
- A. Gersho, and R. Gray. Vector Quantization and Signal Compression.
Boston: Kluwer Academic Publishers, 1992.
Dr. Dapeng Wu
Office: NEB 431
1) Fuyong Xing
Office hour: Friday, 3:00 PM - 4:00 PM. Location: NEB 350.
2) Manish Sapkota
Office hour: Tuesday, 3:00 PM - 4:00 PM. Location: NEB 350.
3) Qiuyuan Huang
Monday, Wednesday, Friday, period 7 (1:55 pm - 2:45 pm)
- Dr. Wu: Monday, Wednesday, period 8 (3 pm - 3:50 pm),
and by appointment
Structure of the Course
The course consists of lectures, 6 homework
assignments, and 1 project.
This course is primarily a lecture course. I cover all important
material in lectures. Since EEL 3135 and EEL 4516 are
prerequisites, I assume some previous knowledge about DSP, probability theory
and stochastic processes, and hence I will cover some material very quickly.
Thus, depending on what and how much you recall from earlier study, varying
amounts of reading in introductory books on DSP, probability theory and
stochastic processes (other than the course textbook) may be necessary; these readings are up to the student.
I will only give reading assignments from the course textbook.
Attending lecture is quite important as I may cover material not available in
any book easily accessible to you. I use Powerpoint presentation during lecture. Lecture
notes will be posted on the course website before the class. The lecture
is to engage the students in independent thinking, critical thinking, and
creative thinking, help the students organize the knowledge around essential
concepts and fundamental principles, and develop conditionalized knowledge
which tells them when, where and why a certain method is applicable to solving
the problem they encounter.
I do not intend for the WWW material to be a substitute for attending lecture
since engaging the students in active thinking, making logical connections
between the old knowledge and the new knowledge, and providing insights are the
objectives of my lecture. The lecture notes are posted on the web so
that you can miss an occasional lecture and still catch up, and it makes taking
- Overview of image processing systems, Image formation and perception,
Continuous and digital image representation
- Image quantization: uniform and nonuniform, visual quantization
- Image contrast enhancement: linear and non-linear stretching, histogram
- Continuous and discrete-time Fourier Transforms in 2D; and linear
convolution in 2D.
- Image smoothing and image sharpening by spatial domain linear filtering;
- Discrete Fourier transform in 1D and 2D, and image filtering in the DFT
- Median filtering and Morphological filtering.
- Color representation and display; true and pseudo color image processing.
- Image sampling and sampling rate conversion (resize).
- Lossless image compression: The concept of entropy and Huffman coding;
Runlength coding for bi-level images; CCITT facsimile compression standards.
- Lossy image compression: Image quantization revisited; Predictive coding;
Transform coding; JPEG image compression standard.
- Imaging Geometry; Coordinate transformation and geometric warping for
- Object recognition
Upon the completion of the course, the student should be able to
- know the fundamental techniques for image processing, video
processing, and computer vision
- understand the basics of analog and digital video: video representation
- acquire the basic skill of designing image/video compression
- familiarize himself/herself with image/video compression standards
Please find handouts here.
- Perfect class attendance is not required, but regular attendance is
- It is the student's responsibility to independently obtain any missed
material (including handouts) from lecture.
- During lecture, cell phones should be turned off.
- No late submissions of your homework solution, and project proposal/report, are allowed
unless U.F. approved reasons are supplied and advance permission is granted by
the instructor. Excused late submissions are consistent with university
policies in the undergraduate catalog (https://catalog.ufl.edu/ugrad/current/regulations/info/attendance.aspx)
and require appropriate documentation.
faculty, staff and student of the University are required and expected to obey
the laws and legal agreements governing software use. Failure to do so can
lead to monetary damages and/or criminal penalties for the individual
violator. Because such violations are also against University policies and
rules, disciplinary action will be taken as appropriate. We, the members of
the University of Florida community, pledge to uphold ourselves and our peers
to the highest standards of honesty and integrity.
- All students are responsible for announcements made in lecture, on the
student access website, or via the class email list.
- It is expected that you will check your email several times per week for
possible course announcements.
- Students with disabilities:
- Students with disabilities requesting accommodations should first
register with the Disability Resource Center (352-392-8565, https://www.dso.ufl.edu/drc)
by providing appropriate documentation. Once registered, students will
receive an accommodation letter which must be presented to the instructor
when requesting accommodation. Students with disabilities should follow this
procedure as early as possible in the semester.
UF students are bound by The Honor
Pledge which states, We, the members of the University of Florida community,
pledge to hold ourselves and our peers to the highest standards of honor and
integrity by abiding by the Honor Code. On all work submitted for credit by
students at the University of Florida, the following pledge is either required
or implied: On my honor, I have neither given nor received unauthorized aid
in doing this assignment. The Honor Code (https://www.dso.ufl.edu/sccr/process/student-conduct-honor-code/)
specifies a number of behaviors that are in violation of this code and the
possible sanctions. Furthermore, you are obligated to report any condition
that facilitates academic misconduct to appropriate personnel. If you have any
questions or concerns, please consult with the instructor or TAs in this
Students are encouraged to discuss
class material in order to better understand concepts. All homework answers
must be the author's own work. However, students are encouraged to discuss
homework to promote better understanding. What this means in practice is that
students are welcome to discuss problems and solution approaches, and in fact
can communally work solutions at a board. However, the material handed in must
be prepared starting with a clean sheet of paper (and the author's
recollection of any solution session), but not refer to any written notes or
existing code from other students during the writing of the solution. In other
words, writing the homework report shall be an exercise in demonstrating the
student understands the materials on his/her own, whether or not help was
provided in attaining that understanding.
All work submitted in this course must be your own and produced exclusively
for this course. The use of sources (ideas, quotations, paraphrases) must be
properly acknowledged and documented. For the copy of the UF Honor Code and
consequences of academic dishonesty, please refer to http://www.dso.ufl.edu/sccr/honorcodes/honorcode.php.
Violations will be taken seriously and are noted on student disciplinary
records. If you are in doubt regarding the requirements, please consult with
the instructor before you complete any requirement of the course.
Students are expected to provide feedback on the quality of instruction in
this course by completing online evaluations at https://evaluations.ufl.edu/evals.
Evaluations are typically open during the last two or three weeks of the
semester, but students will be given specific times when they are open.
Summary results of these assessments are available to students at
All faculty, staff, and students of the University are required and expected
to obey the laws and legal agreements governing software use. Failure to do so
can lead to monetary damages and/or criminal penalties for the individual
violator. Because such violations are also against University policies and
rules, disciplinary action will be taken as appropriate. We, the members of the
University of Florida community, pledge to uphold ourselves and our peers to the
highest standards of honesty and integrity.
There are federal laws protecting your privacy with regards to grades earned
in courses and on individual assignments. For more information, please see:
If you or a friend is in distress,
or 352 392-1575 so that a team member can reach out
to the student.
Counseling and Wellness Center:
392-1575; and the University Police Department: 392-1111 or 9-1-1 for
Recovery Services (SARS)
Student Health Care Center,
Police Department at 392-1111 (or 9-1-1 for emergencies), or
technical support, 352-392-4357 (select option 2) or
e-mail to Learningfirstname.lastname@example.org.
Center, Reitz Union, 392-1601. Career
assistance and counseling.
http://cms.uflib.ufl.edu/ask. Various ways
to receive assistance with respect to using the libraries or finding
Teaching Center, Broward Hall, 392-2010 or 392-6420. General study
skills and tutoring.
302 Tigert Hall,
846-1138. Help brainstorming, formatting,
and writing papers.
||See the course calendar
4pm, December 14
The project report consists of
- (50%) A written report for your project
- (25%) Computer programs that you develop for your
- (10%) Powerpoint file of your presentation
- (15%) Your presentation/demo video on
Top 25% students will receive A. Average score will be at least B+.
More information on UF grading policy may be found at: https://catalog.ufl.edu/ugrad/current/regulations/info/grades.aspx
- Due dates of assignments are specified in the
- No late
submissions are allowed unless U.F. approved reasons are supplied and
advance permission is granted by the instructor.
- If you wish to dispute a
homework grade, you must return the assignment along with a succinct written
argument within one week after the graded materials have been returned to the
class. Simple arithmetic errors in adding up grade totals are an exception,
and can normally be handled verbally on-the-spot during office hours of the
TA. For all other disputes, the entire homework may be (non-maliciously)
re-graded, which may result in increase or decrease of points.
The class project will be done individually (that is, teaming with other
students is not allowed).
Each project requires a proposal and a final report. The final report is expected to be
in the format of a conference paper plus computer programs, a Powerpoint
file, and a video.
Oct. 28, the project proposal (up to 2 pages) is due. On Dec. 14, the
final report (up to 10 pages) is due. For details about the project,
please read here.
Suggested topics for projects are listed here.
Course calendar can be found here.
Related courses in other schools:
George Mason University,
Johns Hopkins University,
Image Compression and Packet Video
University, Digital Video Systems
Stanford University, Digital
University of California, Berkeley,
Processing, Communications and Networking
University of Maryland,
College Park, Digital Image Processing
University of Maryland, College Park,
Multimedia Communication &
Information Security: A Signal Processing Perspective
Anaconda: Anaconda is the
leading open data science platform powered by Python.
Theano is a Python library that lets you to define, optimize, and evaluate
mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).
Keras: Keras is a minimalist,
highly modular neural networks library, written in Python and capable of
running on top of either TensorFlow or Theano. It was developed with a focus
on enabling fast experimentation. Being able to go from idea to result with
the least possible delay is key to doing good research.
Source code for handwritten digit recognition, using deep neural networks
for content aware video processing techniques
Matlab implementation of image/video compression algorithms
Introduction to Matarix Algebra (free book by Autar K Kaw, Professor,
University of South Florida).
- Matrix Reference
- HIPR2: a WWW-based Image
Processing Teaching Materials with J
- Learning by simulations
- Download the following
free (open source)
program to record video with screen capture:
- SD and HD video sequences for
evaluating coding performance of video codec:
- WebRTC: WebRTC is
a free, open-source project that enables web browsers with Real-Time
ATSC (Advanced Television Systems Committee) & HDTV (High Definition
MPEG (Moving Picture Experts Group):
- Virtual Dub: VirtualDub
is a video capture/processing utility for 32-bit Windows platforms
(95/98/ME/NT4/2000/XP), licensed under the GNU General Public License (GPL).
is an efficient multimedia viewer, browser and converter.
- ImageJ: Read and write GIF,
JPEG, and ASCII. Read BMP, DICOM, and FITS. [Open Source, Public Domain]
- Open source for image processing tasks:
- Photosynth: you can access
gigabytes of photos in seconds, view a scene from nearly any angle, find
similar photos with a single click, and zoom in to make the smallest detail as
big as your monitor.
- Video filtering and compression,
by the Video Group, Moscow State University
- MSU Lossless
Video Codec, by the Video Group, Moscow State University
- Computer Vision and
- Digital Signal
Processing: A Review Journal
- Graphical Models and
- Journal of Visual
Commuication and Image Representation
- Real-Time Imaging
- Computers & Graphics
- Data & Knowledge Engineering
- Image and Vision Computing
- Pattern Recognition
- Pattern Recognition Letters
- Signal Processing
- Signal Processing: Image
- IEEE Transactions on
Circuits and Systems for Video Technology
- IEEE Transactions on Multimedia
- IEEE Transactions on
- IEEE Transactions on
- IEEE Transactions on PAMI
Digital Video and Multimedia Standards Pages
Digital TV and DVD
Overview of the AVI format
Public Domain Image Databases
As with MPEG-2
Parts 1 and 2 and
MPEG-4 Part 2 amongst others, the vendors of H.264/AVC products and services
are expected to pay
patent licensing royalties for the patented technology that their products
use. The primary source of licenses for patents applying to this standard is a
private organization known as
MPEG-LA, LLC (which is not affiliated in any way with the MPEG
standardization organization, but which also administers
pools for MPEG-2 Part 1 Systems, MPEG-2 Part 2 Video, MPEG-4 Part 2 Video,
and other technologies).
To search patents, visit free patent searching site: