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.
Title Image Processing
Code ELEC319
Coordinator Dr AA Al Ataby
Electrical Engineering and Electronics
Ali.Al-Ataby@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2021-22 Level 6 FHEQ First Semester 7.5

Aims

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


Pre-requisites before taking this module (other modules and/or general educational/academic requirements):

ELEC270 SIGNALS AND SYSTEMS 

Co-requisite modules:

 

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.


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


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


Teaching Schedule

  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 20

        2

22
Timetable (if known)              
Private Study 53
TOTAL HOURS 75

Assessment

EXAM Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
Formal Exam Standard UoL penalty applies for late submission. Assessment Schedule (When): January  0 hours    100       
CONTINUOUS Duration Timing
(Semester)
% of
final
mark
Resit/resubmission
opportunity
Penalty for late
submission
Notes
             

Reading List

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