University of Florida
Department of Electrical and Computer
Engineering
EEE 6512, Section 012A
Image Processing and Computer Vision
Fall 2021
Course Description
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.
Course Prerequisites
Required Textbook
- Rafael C. Gonzalez, Richard E. Woods, ``Digital Image Processing,'' 3rd Edition, Prentice
Hall; ISBN: 013168728X; August 2007.
or
Recommended Readings
- 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
0130085197.
- 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:
0130175471.
- 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),
ISBN: 0130851981.
- 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
NetLibrary]
- John W. Woods, "Multidimensional Signal, Image, and Video Processing and
Coding," Academic Press; (March 13, 2006), ISBN-10: 0120885166, ISBN-13:
978-0120885169.
- 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
(ISBN 0-13-261108-2).
- 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.
Instructor:
Dr. Dapeng Wu
Office: NEB 431
Email: dpwu@ufl.edu
TA:
1) Haotian Jiang
Email:
haotian.jiang@ufl.edu
2) Tianqi Liu
Email:
tianqi.liu1@ufl.edu
3) Nidish Vashistha
Email:
nidish@ufl.edu
Course website:
http://www.wu.ece.ufl.edu/courses/eee6512f21
Meeting Time
Monday, Wednesday, Friday, period 8 (3 pm - 3:50 pm)
Meeting Room
NEB 100
Office Hours
- Dr. Wu: Monday, Wednesday, period 7 (1:55 pm - 2:45 pm),
and by appointment
via email.
Structure of the Course
The course consists of lectures, 6 homework
assignments, a quiz, 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
notes easier.
Course Outline
- Overview of image processing systems, Image formation and perception,
Continuous and digital image representation
- Image quantization: uniform and nonuniform, visual quantization
(dithering).
- Image contrast enhancement: linear and non-linear stretching, histogram
equalization.
- Continuous and discrete-time Fourier Transforms in 2D; and linear
convolution in 2D.
- Image smoothing and image sharpening by spatial domain linear filtering;
Edge detection.
- Discrete Fourier transform in 1D and 2D, and image filtering in the DFT
domain.
- 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
image registration.
- Object recognition
Course Objectives
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
and transmission
- acquire the basic skill of designing image/video compression
- familiarize himself/herself with image/video compression standards
Handouts
Please find handouts here.

Course Policies
- Attendance:
- Perfect class attendance is not required, but regular attendance is
expected.
- 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 must be consistent with
university policies in the Graduate Catalog (https://catalog.ufl.edu/graduate/regulations)
and require appropriate documentation. Additional information can be found
here:
https://catalog.ufl.edu/graduate/regulations/
-
Software use
- All
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.
- Announcements:
- 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 Requiring Accommodations
-
Students with disabilities who experience learning barriers and would like
to request academic accommodations should connect with the disability
Resource Center by visiting
https://disability.ufl.edu/students/get-started/.
It is important for students to share their accommodation letter with their
instructor and discuss their access needs, as early as possible in the
semester.
-
University
Honesty Policy
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
class.
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.
Course Evaluation
Students are expected to provide professional and
respectful feedback on the quality of instruction in this course by completing
course evaluations online via GatorEvals. Guidance on how to give feedback in
a professional and respectful manner is available at
https://gatorevals.aa.ufl.edu/students/.
Students will be notified when the evaluation period opens, and can complete
evaluations through the email they receive from GatorEvals, in their Canvas
course menu under GatorEvals, or via
https://ufl.bluera.com/ufl/.
Summaries of course evaluation results are available to students at
https://gatorevals.aa.ufl.edu/public-results/.
In-Class Recording
Students are allowed to record
video or audio of class lectures. However, the purposes for which these
recordings may be used are strictly controlled. The only allowable purposes are
(1) for personal educational use, (2) in connection with a complaint to the
university, or (3) as evidence in, or in preparation for, a criminal or civil
proceeding. All other purposes are prohibited. Specifically, students may not
publish recorded lectures without the written consent of the instructor.
A “class lecture” is an educational presentation intended to inform or teach
enrolled students about a particular subject, including any instructor-led
discussions that form part of the presentation, and delivered by any instructor
hired or appointed by the University, or by a guest instructor, as part of a
University of Florida course. A class lecture does not include lab sessions,
student presentations, clinical presentations such as patient history, academic
exercises involving solely student participation, assessments (quizzes, tests,
exams), field trips, private conversations between students in the class or
between a student and the faculty or lecturer during a class session.
Publication without permission of the instructor is prohibited. To “publish”
means to share, transmit, circulate, distribute, or provide access to a
recording, regardless of format or medium, to another person (or persons),
including but not limited to another student within the same class section.
Additionally, a recording, or transcript of a recording, is considered published
if it is posted on or uploaded to, in whole or in part, any media platform,
including but not limited to social media, book, magazine, newspaper, leaflet,
or third party note/tutoring services. A student who publishes a recording
without written consent may be subject to a civil cause of action instituted by
a person injured by the publication and/or discipline under UF Regulation 4.040
Student Honor Code and Student Conduct Code.
Commitment to a Safe and Inclusive Learning Environment
The Herbert Wertheim College of
Engineering values broad diversity within our community and is committed to
individual and group empowerment, inclusion, and the elimination of
discrimination. It is expected that every person in this class will treat one
another with dignity and respect regardless of gender, sexuality, disability,
age, socioeconomic status, ethnicity, race, and culture.
If you feel like your
performance in class is being impacted by discrimination or harassment of any
kind, please contact your instructor or any of the following:
• Your academic advisor or
Graduate Program Coordinator
• Jennifer Nappo, Director of
Human Resources, 352-392-0904,
jpennacc@ufl.edu
• Curtis Taylor, Associate Dean
of Student Affairs, 352-392-2177,
taylor@eng.ufl.edu
• Toshikazu Nishida, Associate
Dean of Academic Affairs, 352-392-0943,
nishida@eng.ufl.edu
Software Use
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.
Student Privacy
There are federal laws protecting your privacy with regards to grades earned
in courses and on individual assignments. For more information, please see:
http://registrar.ufl.edu/catalog0910/policies/regulationferpa.html
Campus Resources:
Health and
Wellness
Covid-19
Protocols:
• You are expected to wear
approved face coverings at all times during class and within buildings even if
you are vaccinated. Please continue to follow healthy habits, including best
practices like frequent hand washing. Following these practices is our
responsibility as Gators.
• If you are sick, stay
home and self-quarantine. Please visit the UF Health Screen, Test & Protect
website about next steps, retake the questionnaire and schedule your test for
no sooner than 24 hours after your symptoms began. Please call your primary
care provider if you are ill and need immediate care or the UF Student Health
Care Center at 352-392-1161 (or email
covid@shcc.ufl.edu) to be evaluated for testing and to
receive further instructions about returning to campus. UF Health Screen, Test
& Protect offers guidance when you are sick, have been exposed to someone who
has tested positive or have tested positive yourself. Visit the
UF Health
Screen, Test & Protect website
for more information.
U
Matter, We Care:
Your well-being is
important to the University of Florida. The U Matter, We Care initiative is
committed to creating a culture of care on our campus by encouraging members
of our community to look out for one another and to reach out for help if a
member of our community is in need. If you or a friend is in distress, please
contact
umatter@ufl.edu so
that the U Matter, We Care Team can reach out to the student in distress. A
nighttime and weekend crisis counselor is available by phone at 352-392-1575.
The U Matter, We Care Team can help connect students to the many other helping
resources available including, but not limited to, Victim Advocates, Housing
staff, and the Counseling and Wellness Center. Please remember that asking
for help is a sign of strength. In case of emergency, call 9-1-1.
Counseling
and Wellness Center:
https://counseling.ufl.edu,
and
392-1575; and the University Police Department: 392-1111 or 9-1-1 for
emergencies.
Sexual
Discrimination, Harassment, Assault, or Violence
If you or a friend has been
subjected to sexual discrimination, sexual harassment, sexual assault, or
violence contact the
Office
of Title IX Compliance, located at Yon Hall Room 427, 1908 Stadium Road,
(352) 273-1094,
title-ix@ufl.edu
Sexual
Assault Recovery Services (SARS)
Student Health Care Center,
392-1161.
University
Police Department
at
392-1111 (or 9-1-1 for emergencies), or
http://www.police.ufl.edu/.
Academic Resources
E-learning
technical support, 352-392-4357 (select option 2) or
e-mail to Learning-support@ufl.edu.
https://lss.at.ufl.edu/help.shtml.
Career Resource
Center, Reitz Union, 392-1601. Career
assistance and counseling.
https://www.crc.ufl.edu/.
Library
Support,
http://cms.uflib.ufl.edu/ask. Various ways
to receive assistance with respect to using the libraries or finding
resources.
Teaching Center, Broward Hall, 392-2010 or 392-6420. General study
skills and tutoring.
https://teachingcenter.ufl.edu/.
Writing Studio,
302 Tigert Hall,
846-1138. Help brainstorming, formatting,
and writing papers.
https://writing.ufl.edu/writing-studio/.
Student Complaints
Campus:
https://www.dso.ufl.edu/documents/UF_Complaints_policy.pdf.
On-Line
Students Complaints:
http://www.distance.ufl.edu/student-complaint-process.

Grading:
Grades |
Percentage |
Due Dates |
Homework |
30% |
See the course calendar |
Project proposal |
10% |
4pm, October
29 |
Quiz |
10% |
December 8 |
Project report |
50% |
4pm, December 15 |
The project report consists of
- (50%) A written report for your project
- (25%) Computer programs that you develop for your
project
- (10%) Powerpoint file of your presentation
- (15%) Your presentation/demo video on
YouTube
Grading scale:
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
Homework:
- Due dates of assignments are specified in the
course calendar.
- No late
submissions are allowed unless U.F. approved reasons are supplied and
advance permission is granted by the instructor. Excused late submissions must
be consistent with university policies in the Graduate Catalog (https://catalog.ufl.edu/graduate/regulations)
and require appropriate documentation. Additional information can be found
here:
https://catalog.ufl.edu/graduate/regulations/
- 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.
Class Project:
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.
On
Oct. 29, the project proposal (up to 2 pages) is due. On Dec. 15, 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,
Computer Vision
Johns Hopkins University,
Image Compression and Packet Video
Polytechnic University,
Video Processing
Purdue
University, Digital Video Systems
Stanford University, Digital
Video Processing
University of California, Berkeley,
Multimedia Signal
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
Useful links
- Anaconda: Anaconda is the
leading open data science platform powered by Python.
- Theano:
Theano is a Python library that lets you to define, optimize, and evaluate
mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).
- TensorFlow: TensorFlow
is an open source software library for numerical computation using data flow
graphs. Nodes in the graph represent mathematical operations, while the graph
edges represent the multidimensional data arrays (tensors) communicated
between them. The flexible architecture allows you to deploy computation to
one or more CPUs or GPUs in a desktop, server, or mobile device with a single
API.
- 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.
- PyTorch: PyTorch is a deep learning
framework for fast, flexible experimentation.
- A curated list of resources dedicated to
recurrent neural networks
-
Source code in Python for handwritten digit recognition, using deep neural
networks: [another
link]
- Source
code in PyTorch for handwritten digit recognition, using deep neural
networks
- Source code in Python for
TF-mRNN: a TensorFlow library for image captioning
- Source code in Python for the following work on image captioning:
- Image captioning:
- Microsoft COCO datasets
- Visual Question Answering:
- Semantic Propositional Image Caption Evaluation (SPICE)
- Region-based Convolutional Neural Networks (R-CNN)
- References:
- Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. "Faster R-CNN:
Towards real-time object detection with region proposal networks." In
Advances in neural information processing systems, pp. 91-99. 2015. [pdf]
- Dai, Jifeng, Yi Li, Kaiming He, and Jian Sun. "R-FCN: Object detection
via region-based fully convolutional networks." In Advances in neural
information processing systems, pp. 379-387. 2016. [pdf]
[source code]
- Huang, Jonathan, Vivek Rathod, Chen Sun, Menglong Zhu, Anoop
Korattikara, Alireza Fathi, Ian Fischer et al. "Speed/accuracy trade-offs
for modern convolutional object detectors." arXiv preprint
arXiv:1611.10012 (2016). [pdf]
(E.g., for Inception V3, extract features from the “Mixed 6e” layer whose
stride size is 16 pixels. Feature maps are cropped and resized to 17x17.)
- Source codes:
- Source code in Python for end-to-end training of LSTM
- Bidirectional Encoder Representations from Transformers (BERT)
- Source code in Python for sequence-to-sequence learning (language
translation, chatbot)
- AI City Challenge
- Visual Storytelling Dataset (VIST)
- Visual storytelling algorithms:
- No Metrics Are Perfect: Adversarial REward Learning for Visual
Storytelling: source codes (TensorFlow)
- Visual Genome is a dataset, a
knowledge base, an ongoing effort to connect structured image concepts to
language.
-
MPII Movie & Description dataset for automatic video description, video
summary, video storytelling
- Bidirectional recurrent neural networks (B-RNN):
- Graves, Alan, Navdeep Jaitly, and Abdel-rahman Mohamed. "Hybrid speech
recognition with deep bidirectional LSTM." IEEE Workshop on Automatic Speech
Recognition and Understanding (ASRU), 2013. [pdf]
- Deep reinforcement learning
- UCL Course on reinforcement learning: [ppt]
[video]
- References:
- Mnih, Volodymyr, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis
Antonoglou, Daan Wierstra, and Martin Riedmiller. "Playing
atari with deep reinforcement learning." arXiv preprint
arXiv:1312.5602 (2013).
- Mnih, Volodymyr, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel
Veness, Marc G. Bellemare, Alex Graves et al. "Human-level
control through deep reinforcement learning." Nature 518, no.
7540 (2015): 529-533. [source
code]
-
How to Study Reinforcement Learning
- Source codes:
-
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym,
Tensorflow. Exercises and Solutions to accompany Sutton's Book and David
Silver's course. [link]
- Generative Adversarial Network (GAN)
- References:
- Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley,
Sherjil Ozair, Aaron Courville, and Yoshua Bengio. "Generative
adversarial nets." In Advances in neural information processing
systems, pp. 2672-2680. 2014.
- Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised
representation learning with deep convolutional generative adversarial
networks." arXiv preprint arXiv:1511.06434 (2015).
- Arjovsky, Martin, Soumith Chintala, and Léon Bottou. "Wasserstein
GAN." arXiv preprint arXiv:1701.07875 (2017).
- Types of GAN
- Vanilla GAN
- Conditional GAN
- InfoGAN
- Wasserstein GAN
- Mode Regularized GAN
- Coupled GAN
- Auxiliary Classifier GAN
- Least Squares GAN
- Boundary Seeking GAN
- Energy Based GAN
- f-GAN
- Generative Adversarial
Parallelization
- DiscoGAN
- Adversarial Feature
Learning & Adversarially
Learned Inference
- Boundary Equilibrium GAN
- Improved Training for
Wasserstein GAN
- DualGAN
- MAGAN: Margin Adaptation
for GAN
- Softmax GAN
- Source codes:
- A Tensorflow
Implementation of "Deep Convolutional Generative Adversarial Networks":
python code
- Collection
of generative models, e.g. GAN, VAE in Pytorch and Tensorflow:
python code
- Sequential Generative Adversarial Network (GAN)
- References:
- Yu, Lantao, Weinan Zhang, Jun Wang, and Yong Yu. "SeqGAN:
Sequence Generative Adversarial Nets with Policy Gradient." In AAAI,
pp. 2852-2858. 2017.
- Mogren, Olof. "C-RNN-GAN:
Continuous recurrent neural networks with adversarial training." arXiv
preprint arXiv:1611.09904 (2016).
- Im, Daniel Jiwoong, Chris Dongjoo Kim, Hui Jiang, and Roland Memisevic.
"Generating images with
recurrent adversarial networks." arXiv preprint arXiv:1602.05110
(2016).
- Press, Ofir, Amir Bar, Ben Bogin, Jonathan Berant, and Lior Wolf. "Language
Generation with Recurrent Generative Adversarial Networks without
Pre-training." arXiv preprint arXiv:1706.01399 (2017).
- Source codes:
-
Subjective evaluation
for content aware video processing techniques
-
Cancer imaging
archive: TCIA data are organized as “collections”; typically these are
patient cohorts related by a common disease (e.g. lung cancer), image modality
or type (MRI, CT, digital histopathology, etc) or research focus.
-
MATLAB Tutorial
-
MATLAB Central
-
Matlab Primer,
Matlab Manuals,
Image
Processing Toolbox
-
Matlab implementation of image/video compression algorithms
-
Introduction to Matarix Algebra (free book by Autar K Kaw, Professor,
University of South Florida).
- Matrix Reference
Manual
- HIPR2: a WWW-based Image
Processing Teaching Materials with J
- LIDAR
- Learning by simulations
- OpenCV
- OpenGL
- Download the following
free (open source)
program to record video with screen capture:
http://www.nchsoftware.com/capture/index.html?gclid=CNadwsW6-6wCFSVjTAodbjzTSg
- SD and HD video sequences for
evaluating coding performance of video codec:
http://media.xiph.org/video/derf/
- WebRTC: WebRTC is
a free, open-source project that enables web browsers with Real-Time
Communications (RTC) capabilities via simple JavaScript APIs.
The Missing Semester of Your CS
Education
Standards:
ATSC (Advanced Television Systems Committee) & HDTV (High Definition
Television):
MPEG (Moving Picture Experts Group):
Software:
-
Video codec
- 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).
- XnView:
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:
http://octave.sourceforge.net/doc/image.html
- 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
HSI color
model
Compression link:
http://cchen1.et.ntust.edu.tw/compression/compression.htm
JOURNALS
Elsevier
- Computer Vision and
Image Understanding
- Digital Signal
Processing: A Review Journal
- Graphical Models and
Image Processing
- 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
Communication
IEEE
- IEEE Transactions on
Circuits and Systems for Video Technology
- IEEE Transactions on Multimedia
- IEEE Transactions on
Image Processing
- IEEE Transactions on
Medical Imaging
- IEEE Transactions on PAMI
Kluwer
SPIE
Digital Video and Multimedia Standards Pages
Digital TV and DVD
Overview of the AVI format
Computer Vision
Public Domain Image Databases
CMU Database
Patent licensing
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
patent
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:
www.FreePatentsOnline.com.
