EEE3013 : Image Processing and Machine Vision
- Offered for Year: 2020/21
- Module Leader(s): Professor Satnam Dlay
- Owning School: Engineering
- Teaching Location: Newcastle City Campus
Semesters
| Semester 2 Credit Value: | 10 |
| ECTS Credits: | 5.0 |
Aims
To introduce the students to the fundamentals of image processing and provide them with the essential knowledge about the main themes, so that, in the future, the students will be able to readily apply their knowledge in industry or research or further enhance it by self study. The Course is divided into two sections: Image Processing and Machine Vision.
Outline Of Syllabus
Image Processing and Computer Vision Background, Image Processing and Computer Vision
Applications Digital Image Processing Hierarchy: Human Perception of Pictures, Digital Image Processing Hardware. Image Model, Amplitude digitisation: Intensity Quantisation, Spatial coordinate digitisation: Image Sampling, Image Quality, Image Pixel Relationships.
Linear Operators, 2-D Transforms. Spatial Domain Methods (filters), Frequency Domain Methods. Inverse Filtering.
Image. Compression, Redundancy Types, Lossless and Lossy Compression, Image Compression
Standards.
Object Detection Methods, Edge Liking and Boundary Detection
Thresholding Methods, Region Oriented Methods. Matrix manipulation; Hotelling algorithm, Karhunen–Loève compression
Object Representation and Description, Representation schemes,
Description. Pattern Recognition, Decision Theoretic Methods for Recognition.
Case Study Lectures.
Use of Software such as MATLAB/Python to demonstrate Image processing and Machine Vision Techniques.
Teaching Methods
Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 15:00 | 15:00 | Formative and summative assessment |
| Scheduled Learning And Teaching Activities | Lecture | 27 | 0:20 | 9:00 | Non-synchronous: Replacing lectures with similar material |
| Scheduled Learning And Teaching Activities | Small group teaching | 9 | 1:00 | 9:00 | Online synchronous tutorial and Q&A sessions. |
| Guided Independent Study | Independent study | 1 | 58:00 | 58:00 | Student study time: reviewing lecture notes; general reading |
| Guided Independent Study | Independent study | 27 | 0:20 | 9:00 | Student study time of non-synchronous material |
| Total | 100:00 |
Teaching Rationale And Relationship
Outcomes are achieved through online sessions and PiP teaching.
Lectures provide core material and guidance for further reading, problem solving practice is integrated into lectures.
Alternative will be offered to students who are unable to be present in person.
Students should consult their individual timetable for up-to-date delivery information.
Assessment Methods
Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.
The format of resits will be determined by the Board of Examiners
Exams
| Description | Length | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|---|
| Written Examination | 120 | 2 | A | 100 | Choice of 3 question from 2 from Image processing and 2 from machine vision |
Formative Assessments
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Written exercise | 1 | M | Tutorial Question on Image Processing set in the middle of the semester |
Assessment Rationale And Relationship
Lectures provide the core material as well as guidance for further reading. Problem practice is integrated into lecture structure, examples are given in the use of the software tools.
The examination provides the opportunity for the students to demonstrate their understanding of the course material and their ability to apply critical thinking. The problem solving aspects of the assessment enable the student to demonstrate that they are able to apply this understanding and their analysis and synthesis skills to novel situations.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- EEE3013's Timetable