Module Specification

The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
1. Module Title Image Processing
2. Module Code ELEC319
3. Year Session 2023-24
4. Originating Department Electrical Engineering and Electronics
5. Faculty Fac of Science & Engineering
6. Semester First Semester
7. CATS Level Level 6 FHEQ
8. CATS Value 7.5
9. Member of staff with responsibility for the module
Mr Al-Irhayim Electrical Engineering and Electronics Ahmed.Al-Irhayim2@liverpool.ac.uk
10. Module Moderator
11. Other Contributing Departments  
12. Other Staff Teaching on this Module
Mrs J Birtall School of Electrical Engineering, Electronics and Computer Science Judith.Birtall@liverpool.ac.uk
Dr W Al-Nuaimy Electrical Engineering and Electronics Wax@liverpool.ac.uk
13. Board of Studies
14. Mode of Delivery
15. Location Main Liverpool City Campus
    Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
16. Study Hours 20

        2

22
17.

Private Study

53
18.

TOTAL HOURS

75
 
    Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other
19. Timetable (if known)            
 
20. Pre-requisites before taking this module (other modules and/or general educational/academic requirements):

ELEC270 Signals and Systems
21. Modules for which this module is a pre-requisite:

 
22. Co-requisite modules:

 
23. Linked Modules:

 
24. Programme(s) (including Year of Study) to which this module is available on a mandatory basis:

25. Programme(s) (including Year of Study) to which this module is available on a required basis:

26. Programme(s) (including Year of Study) to which this module is available on an optional basis:

27. Aims
 

To introduce the basic concepts of digital image processing and pattern recognition.

 
28. Learning Outcomes
 

(LO1) Knowledge and understanding of Human Vision

 

(LO2) Knowledge and understanding of Image Histogram and its application

 

(LO3) Knowledge and understanding of Image Transformation methods and their applications

 

(LO4) Knowledge and understanding of Shapes and Connectivity

 

(LO5) Knowledge and understanding of Morphologocal Operations and their applications

 

(LO6) Knowledge and understanding of Noise Filtering methods in Image Processing

 

(LO7) Knowledge and understanding of Image Enhancement techniques

 

(LO8) Knowledge and understanding of Image Segmentation and its applications

 

(LO9) Knowledge and understanding of Image Compression methods

 

(LO10) Knowledge and understanding of Frequency Domain Image Analysis

 

(S1) On successful completion of the module, students should be able to show experience and enhancement of the following key skills: Independent learning Problem solving and design skills

 

(S2) After successful completion of the module, the student should have: The ability to apply relevant image enhancement techniques to a given problem. The necessary mathematical skills to develop standard image processing algorithms. The necessary Software skills (using MATLAB) to apply image processing methods and techniques on images.

 
29. Teaching and Learning Strategies
 

COVID-19 Era Teaching and Learning Methods:

Option a. Hybrid delivery, with social distancing on campus

Teaching Method 1 - Online Asynchronous Lectures
Description: Lectures to explain the material
Attendance Recorded: No
Notes: On Average Two Per Week

Teaching Method 2 - On-campus Tutorials with social distancing
Description: Tutorials on the Assignments and Problem Sheets
Attendance Recorded: Yes
Notes: On Average One Per Week

Teaching Method 3 - Formative Assessment
Description: Online tests to check background and module pre-requisites and to check student understsnding
Attendance Recorded: No
Notes: Online Tests on CANVAS

Option b. Fully on-line delivery and assessment

Teaching Method 1 - Online Asynchronous Lectures
Description: Lectures to explain the material
Attendance Recorded: No
Notes: On Average Two Per Week

Teaching Method 2 - Online Synchronous Tutorials
Description: Tutoria ls on the Assignments and Problem Sheets
Attendance Recorded: Yes
Notes: On Average One Per Week

Teaching Method 3 - Formative Assessment
Description: Online tests to check background and module pre-requisites and to check student understsnding
Attendance Recorded: No
Notes: Online Tests on CANVAS

Option c. Standard on-campus delivery with minimal social distancing

Teaching Method 1 - On-campus Lectures
Description: Lectures to explain the material
Attendance Recorded: Yes
Notes: Three Per Week

Teaching Method 2 - On-campus Tutorials
Description: Tutorials on the Assignments and Problem Sheets
Attendance Recorded: Yes
Notes: On Average One Per Week

Teaching Method 3 - Formative Assessment
Description: Online tests to check background and module pre-requisites and to check student understsnding
Attendance Recorded: No
Notes: Online Tests on CANVAS

 
30. Syllabus
   

Introduction to Image Processing
Human Vision
Image Transformations
Shapes and Connectivity
Morphological Operations
Noise Filtering
Image Enhancement
Edge Detection
Image Segmentation
Image Compression
Frequency Domain Image Analysis

 
31. Recommended Texts
  Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.
 

Assessment

32. EXAM Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
  Written Exam Assessment Schedule (When): January 0 100
33. CONTINUOUS Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes