To registered students: As the class is full and there
are many students on the waiting list, we are attempting
to determine which students are actually interested in
continuing in the class. If a registered student has not
attended the August 25 class and does not attend the
August 27, he/she will be automatically dropped from the
class. Absences for good reason should be communicated
to the instructor prior to August 27. Attendance will
be determined by marking the sign up sheet circulated
in the class. No action is required from those attending
the class.
To students on wait list: It is not possible to expand
the size of the class due to physical space limitations.
It is not allowed for students to take the class on
web without becoming a DEN student. As you can see,
we are making effort to find registered students who
do not intend to actually take the class. Meanwhile,
you are advised to continue to come to the class,
provided that there are enough seats for you.
Please See Main Class WEB page at http://den.usc.edu/ for lectures, assignments and
handouts.
CSCI 455 or equivalent - Data Structures, good programming
skills. Ability to convert informal descriptions into computer algorithms.
Students must be able to program in C or C++.
2.
Basic Mathematics - Knowledge of and ability to use
calculus, analytical solid geometry and linear algebra (matrix theory) is
essential. Knowledge of elementary probability theory will also be needed.
If you have not used these skills for several years, you must be prepared to learn them rapidly.
3.
CSCI 561 and 573 (Artifical Intelligence) are
helpful but NOT required.
Course Objective
The objective of this course is to understand the basic issues in
computer vision and major approaches that address them. Even though Computer Vision is being
used for many practical applications today, it is still not a "solved" problem.
Hence, definitive solutions are available only
rarely; most of the time, we will discuss alternatives and their
limitations. After completing the course, the students may expect to have
the knowledge needed to read and understand the more advanced topics and
current research literature, and the ability to start working in
industry or in academic research. However, this course is NOT
designed to be a "cookbook" course that gives just a survey of the
methods needed in "practice", nor will it cover "commercial" systems in
any detail.
Course Requirement
There will be two exams:
Exam1: Scheduled on Monday, October 13th.
Exam2: on the last day of the class, December 3rd. (subject to student approval)
Both exams will be conducted during class hours. Exam1 will count for 25%, and Exam2 will count for 35% of the
course grade. Homework assignments, written and
programming together, will also count for 30% of the grade. 10% of the
weight will be given to class attendance and participation (except for
remote students). Note that all assignments are considered an intergral
part of the course and MUST be completed. Not completing assignments may
result in "F" grade.
In this course we encourage students to study together. This includes
discussing general strategies to be used for individual assignments.
However, all work submitted for the class is to be done individually,
unless an assignment specifies otherwise.
Some examples of what is not allowed by the conduct code: copying all
or part of someone else's work, and submitting it as your own; giving
another student in the class a copy of your assignment solution;
consulting with another student during an exam. If you have questions
about what is allowed, please discuss it with the instructor.
Violations of the Student Conduct Code will be filed with the Office of
Student Conduct, and appropriate
sanctions will be given.
Programming Facility
A software library of basic image processing algorithms, called
OpenCV, will be used in programming assignments; this library is available
for free download for educational purpose. This library is available for
MS Windows and Linux; however, we will only provide TA support for the
windows version. Students may choose to complete assignments using USC
computer facilities or their own PCs. OpenCV can be downloaded here.
Following is a list of topics expected to be covered, in anticipated
order, and with expected time to be spent on them. This list is intended
to be only indicative, the actual topics, the order and the time may vary
somewhat depending on various factors including student interests and
preparation.
Introduction (1 week) Background, requirements
and issues, human vision.
Image formation: geometry and photometry (2 weeks) Geometry, brightness, color, Camera calibration
Image segmentation (2 weeks) Region
segmentation, Edge and line finding
Multi-view Geometry (3 weeks) Shape from stereo
and motion, feature matching, surface fitting, Active ranging
Image classification(2 weeks) Pixel
classification, region classification, face detection and identification
Please submit hard copies in class on the due day.
The University of Southern California does not screen or control the
content on this website and thus does not guarantee the accuracy,
integrity, or quality of such content. All content on this website is
provided by and is the sole responsibility of the person from which
such content originated, and such content does not necessarily reflect
the opinions of the University administration or the Board of Trustees
The University of Southern California does not screen or control the content on this website and thus does not guarantee the accuracy, integrity, or quality of such content. All content on this website is provided by and is the sole responsibility of the person from which such content originated, and such content does not necessarily reflect the opinions of the University administration or the Board of Trustees
