ELEC319

Image Processing

Aims

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

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

Recommended Texts

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

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.

Learning Strategy

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