Miguel Á. Carreira-Perpiñán
Dept. of Computer Science & Electrical Engineering
OGI School of Science & Engineering / OHSU
Office: Bronson Creek Building 150J
Office hours: by appointment (call or e-mail, including [EE589] in the subject).
Mondays and Wednesdays 7-8:30pm in classroom 401, Paul Clayton Building (number 2 in the map)
New location and time:Mondays and Wednesdays 12:30-2pm in classroom 403, Wilson Clark Center (number 3 in the map).
Course web page: http://www.csee.ogi.edu/~miguel/ee589
This course is partially supported by Intel.
This course provides an overview of computer vision. Computer vision deals with the problem of recovering information about the world from one or more images. This course covers the basic problems and techniques of computer vision, including the process of image formation (lighting and camera models), multiview geometry (stereo, affine and projective reconstruction), robust estimation, probabilistic models, segmentation, tracking and object recognition. Although the course will be a general survey, sufficient detail will be given for students to be able to build useful applications. Particular mention will be made of health-related applications such as medical image segmentation, image retrieval in digital libraries, or unobstrusive patient monitoring.
The course does not cover image processing aspects (such as image filtering, Fourier transforms, image pyramids, edge detection, image enhancement, restoration and compression), which are covered in course EE 584 / EE 684 Introduction to image processing.
The course will also be useful for students interested in machine learning, (biomedical) image processing, computer graphics, robotics and human-computer interaction.
Prerequisites: undergraduate engineering maths, specifically calculus and linear algebra. Some knowledge of probability will be necessary for part of the course; a background chapter in the textbook's web site covers this.
The following courses are useful complements (but not necessary for the course):
Required textbook (see the supporting material):
Other recommended books:
If you want to refresh your knowledge of linear algebra and multivariate calculus, the following are helpful (any edition or similar book is fine):
Before each class, you should have read the corresponding part of the textbook and the notes. I will teach the material in the order below.
The course grading will be based on three projects, as follows:
While I encourage you to discuss your work with other students, the projects must be the result of your own work without collaboration. There is no final exam.
If you have never used Matlab, there are many online tutorials, for example:
OHSU | OGI | Dept. of CSEE | Adaptive Systems Group | MACP's Home Page