This course will introduce fundamental technologies for digital image and video representation, compression, analysis, and processing. Students will gain understanding of algorithm and system design, analytical tools, and practical implementations of various digital image applications.
Topics include digital image/video perception, sampling, optimal quantization, halftoning, transform, filtering, multi-spectral processing, restoration, analysis, feature extraction, morphological transform, coding, segmentation, and 3D model reconstruction. Considerations of practical system requirements (e.g., medical, satellite, consumer) will be discussed as well. We will also have hands on experience in applying analytical solutions in practical applications.
The course assumes knowledge about signals and systems, as well as a basic familarity of linear algebra and probability.
The course consists of lectures each week, twelve homeworks (eight analytical, four experimental), a midterm (2.5 hrs) and a final (3 hrs). The grade will be broken down as follows:
Late policy for homeworks, applying to both the analyticals and experimentals.
You have an option to submit the HWs beyond the due date (but before the solutions are out), they will be graded with the same standard but will be multiplied by the following damping factors (as a function of time) when it counts towards the overall grade.
1 day (1-24 hrs): 0.9
Lecture notes, HWs and other course materials will be announced on the
course outline page of this web site directly.
Half of the HWs will include practical problem that will be run under the numerical computation package Matlab, or other computer language of your choice.
Important announcements and class updates will be sent to the class either via email and/or the course webpage. Please check both regularly.
Please put "[DIP-E4830]" in the title of any email correspondences
you send with the instructors or the TA. Thank you.
Lexing Xie <xlx at ee.columbia.edu>
Last updated: 2007-01-16