Students are introduced to computer vision techniques starting with
basic digital image processing. Concepts covered will include image
convolution, edge detection, and the Fourier and Wavelet transforms.
The course will also introduce students to video processing, including
camera calibration and concepts related to Augmented Reality,
including marker detection and camera homography.
Computer Vision: Algorithms and Applications,
(c) Richard Szeliski,
Principles of Digital Image Synthesis,
(c) Andrew Glassner,
Dr. Andrew Duchowski
||McAdams 309, 656-7677, email@example.com
To gain a basic understanding of computer vision algorithms and
then to (install and) use OpenCV.
||This is not a lab course.
||Unless otherwise specified, these are individual
assignments, and must be strictly your own work and are not to
be shown to anyone else.
||Roll will be taken for the first one or two weeks while
the class roll fluctuates. However, attendance is not required.
Absence, excused or not, does not change the responsibility for
Tests missed due to excused absences will normally result
in the test not being counted in the average grade (i.e., there will
normally be no makeup tests). An unexcused absence from a
test will normally result in a grade of zero for that test. Students
are expected to give at least one week advance notice for excused
||The University policies on academic dishonesty apply.
Publicly-available code or other material may be freely used if
appropriately attributed. Each student is responsible for protecting
his or her files from access by others. Work that is essentially
the same and submitted without proper attribution is considered
to be a violation of academic dishonesty policy by all those
submitting the work, regardless of who actually did the work.
||Students are expected to wait for 15 minutes after
the class beginning time before leaving if the instructor is late.
|Assignment late policy:
||Late assignments will be accepted but points will be
deducted according to the formula (3n)3 where
n is the number of days late. Example: assuming assignments
are due on Wednesday, the point deduction is as follows:
|Max points possible
|100||Wed.||0 (due date)|
| 73||Thu.||1 day late|
| 0||Fri.||2 days late|
Late assignments will receive lowest priority for grading and
- Image I/O
- Image Convolution with Sobel (FIR) Filter
- Edge Detection
- Fourer Transform
- Wavelet Transform
- Video I/O
- Camera Calibration / Homography
- Fiducial Marker Detection & Tracking
- Face Detection & Tracking
- Hotelling (Karhunen-Loève) Transform
- Principal Component Analysis (PCA)
- Computational Photography
- High Dynamic Range (HDR) Tone Mapping with Bilateral (IIR) Filter