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CMPE 362

Course ID:
Course Code & Number
CMPE 362
Course Title
Digital Image Processing
Level
BS
Credit Hours/ ECTS Credits
(3+0+0) 3 TEDU Credits, 5 ECTS Credits
Year of Study:
Junior
Semester:
Fall
Type of Course:
Elective
Mode of Delivery:
Face-to-face
Language of Instruction:
English
Pre-requisite / Co-requisite::
Pre-requisites: MATH 203
Co-requisites: NONE
Catalog Description
Image model sampling and quantization. Basic relationships between pixels and image geometry. Two-dimensional Fourier transforms. Image enhancement. Spatial and frequency domain methods. Image restoration. Image segmentation.
Course Objectives

The objective of this course is to provide effective skills for developing and applying image processing methods and tools to obtain significant information from images.

Software Usage
Matlab
Course Learning Outcomes

Upon succesful completion of this course, a student will be able to
1. Describe the basic theory of image processing 
2. Examine binary and color images using image processing tools 
3. Analyze and apply image enhancement methods in spatial and frequency domain
4. Analyze and apply image segmentation methods 
5. Analyze and apply algorithms to extract significant information from images 
6. Develop solution methods for image processing problems using the state of the art 

Learning Activities and Teaching Methods:
Telling/Explaining Discussion/Debate Questioning Reading Demonstrating Problem Solving Collaborating Think-Pair-Share Others
Assessment Methods and Criteria:
Test / Exam Quiz Others
Assessment Methods and Criteria Others:
Design Content
Recommended Reading
1. Computer Vision: Algorithms and Applications by R. Szeliski, Springer, 2010. (electronic draft available) 2. Computer Vision by L. G. Shapiro and G. C. Stockman, Prentice Hall, 2001.
Required Reading
1. Digital Image Processing (3rd Edition) by Rafael C. Gonzalez and Richard E. Woods
Grading

Assignments - 45% 
Midterm - 20% 
Final Exam - 30% 
Class participation - 5%

Learning Activities and Teaching Methods Others:
Course Coordinator:
Student Workload:
WorkloadHrs
Course & Program Learning Outcome Matching: