1. WXGE 6103 Digital Image Processing Semester 2, Session 2013/2014

2. Agenda Lecturer Information. Course Overview / Requirements. Introduction to Image Processing.

3. Contact Information Lecturer: Mogeeb Mosleh Email: MogeebMosleh@Gmail.comMogeebMosleh@Gmail.com

4. Course Description Course Code: Course Title: Digital Image Processing. Course Type: Core Course for (Telecommunication Department)

5. Course Contents Introduction to DIP. Digital Image Fundamentals (1). Digital Image Fundamentals (2). Image Enhancement in the Spatial Domain. Image Enhancement in the Frequency Domain. Image Restoration. Color Image Processing. Image Compression. Morphological Image Processing. Image Segmentation. Representation and Description Pattern & Object Recognition.

6. Course Objectives This module aims to: Develop a theoretical foundation of fundamental Digital Image Processing concepts. Provide mathematical foundations for digital manipulation of images; image acquisition; preprocessing; segmentation; Fourier domain processing; and compression. Gain experience and practical techniques to write programs using MATLAB language for digital manipulation of images; image acquisition; preprocessing; segmentation; Fourier domain processing; and compression.

7. Expected Learning Outcomes 1- Knowledge and understanding: Have a clear understanding of the principals the Digital Image Processing terminology used to describe features of images. Have a good understanding of the mathematical foundations for digital manipulation of images; image acquisition; preprocessing; segmentation; Fourier domain processing, compression and analysis. Be able to write programs using Matlab language for digital manipulation of images; image acquisition; preprocessing; segmentation; Fourier domain processing; and compression. Have knowledge of the Digital Image Processing Systems. Be able to understand the documentation for, and make use of, the MATLAB library and MATLAB Digital Image Processing Toolbox (IPT). Learn and understand the Image Enhancement in the Spatial Domain. Learn and understand the Image Enhancement in the Frequency Domain. Understand the Image Restoration, Compression, Segmentation, Recognition, Representation and Description.

8. Expected Learning Outcomes 2- Cognitive skills (thinking and analysis): Be able to use different digital image processing algorithms. Be able to design, code and test digital image processing applications using MATLAB language. Be able to use the documentation for, and make use of, MATLAB library and MATLAB Digital Image Processing Toolbox (IPT). Analyze a wide range of problems and provide solutions related to the design of image processing systems through suitable algorithms, structures, diagrams, and other appropriate methods. Practice self-learning by using the e-courses and web materials.

9. Expected Learning Outcomes 3- Communication skills (personal and academic). Display personal responsibility by working to multiple deadlines in complex activities. Be able to work effectively alone or as a member of a small group working on some programming tasks. 4- Practical and subject specific skills ( Transferable Skills) Plan and undertake a major individual image processing project. Be able to work effectively alone or as a member of a small group working on some programming tasks. Prepare and deliver coherent and structured verbal and written technical reports Use laboratory equipment effectively. Use the scientific literature effectively.

10. Course Resources 1. “Digital Image Processing”, 3 rd edition, Rafael C. Gonzalez, Richard E. Woods, Prentice Hall 2. “Digital Image Processing Using Matlab”, Rafael C. Gonzalez, Richard E. Woods, Prentice Hall 3. “Digital Image Processing”, 2 nd edition, K.R. Castleman, Prentice Hall 4. “Computer Vision & Image Processing”, Scott E. Umbaugh, Prentice Hall

11. Course Requirements - Recap 5% Attendance/ Participation 15% Assignments / Tutorial 10% Mid term Exam 20% Group Project/ Presentation 50% Final Exam

12. Evaluation & Weightage Continuous Assessments : 50% Attendance / Participation: 5% Assignments/Tutorials: 15% Mid Semester Exam: 10% Group Project/ Presentations: 20% Final Examination: 50%

13. Attendance/Participation Attendance Attendance must be 80% or more. Absent – please provide a letter. Absent > 3 times – provide a reasonable reasons. Bar from exam. Switch off hand phone during lecture. Participation Active Student. 5%

14. Assignments/Tutorials You will be assigned a number of related tutorials. You will need to read and answer them and return soft and hard copies of your answers. Tutorial tasks are very important for understanding the concepts and applying them in real scenarios. Projects and assignments – on time 15%

15. Mid Term Exam You will be given ONE MID TERM EXAM on certain topics that will be covered in the lectures. Format will be discussed later. Topics covered will be announced later. Time: After Mid Semester Break. 10%

16. Group Project/Presentation Self-select into groups (2-3 students each) Each group will be hired to develop a system. You will have to explore the assigned system and follow the software engineering process in developing your system. You can use any programming language and/or tool to implement and develop the assigned system. Your group project MUST be documented well and a report must be submitted in soft and hard copies. Prepare a 20 to 25 minutes presentation and demo. 20%

17. Format of Submission Hard copy Word-processed Include the following information: Title of assignment & (due date) Your name + (Matrics number) Your e-mail address Lecturer’s name To be submitted to me during class.

18. Final Exam Give certain scenario and apply concepts that have been learnt throughout the semester. Includes material from entire semester? All materials are important for the final exam 50%

19. Any Questions?