Digital Image Processing I
Learning Objective:To cover the basic analytical methods which are widely used in image processing; to cover issues and technologies which are specific to images and image processing systems; to develop experience with using computers to process images.
Deterministic and stochastic modeling of images, linear and nonlinear filtering, and image transformations for coding and restoration. A variety of web based laboratory experiments based on a combination of Matlab and C programming environments will be used.
Spring 2019 Syllabus
Topics Covered:1. Continuous Parameter Signals and Systems 2. Discrete Parameter Signals and Systems 3. Image topology and segmentation 4. Imaging Perception and Representation 5. Resolution conversion 6. Image Enhancement and Filtering (Lab) 7. Image Quantization and Halftoning 8. Image Coding (Lab) 9. Image Reconstruction
Prerequisites:Well prepared students should have a background in at least two out of the three following topics: linear time-invariant systems theory including the Fourier transform; random variables and random processes; and computer programming in the C programming language. Most background material is covered in the course, but at a rapid pace.
Applied / Theory:
Homework:8 laboratory assignments will be assigned during the semester with the average laboratory requiring approximately 8 hours to complete.
Exams:Two exams and one final exam.
Textbooks:Official textbook information is now listed in the Schedule of Classes. NOTE: Textbook information is subject to be changed at any time at the discretion of the faculty member. If you have questions or concerns please contact the academic department.
REQUIRED TEXTBOOK: "Handbook of Image and Video Processing (Communications, Networking and Multimedia) Alan C. Bovik Academic Press 9780121197902 1st Edition "