EEE3013 : Image Processing and Machine Vision
- Offered for Year: 2019/20
- Module Leader(s): Professor Satnam Dlay
- Owning School: Engineering
- Teaching Location: Newcastle City Campus
|Semester 2 Credit Value:||10|
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.
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 co-ordinate digitisation: Image Sampling, Image Quality, Image Pixel Relationships, Linear Operators, 2-D Transforms. Spatial Domain Methods, 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. Object Representation and Description, Representation schemes, Description. Pattern Recognition, Decision Theoretic Methods for Recognition.
Case Study Lectures
MATLAB Iage Processing Toolbox
|Guided Independent Study||Assessment preparation and completion||24||0:30||12:00||Revision for final examination|
|Guided Independent Study||Assessment preparation and completion||1||2:00||2:00||Final exam|
|Scheduled Learning And Teaching Activities||Lecture||24||1:00||24:00||N/A|
|Guided Independent Study||Independent study||1||62:00||62:00||Reviewing lecture notes; general reading|
Jointly Taught With
|EEE8028||Image Processing and Machine Vision|
|EEE8098||Image Processing and Computer Vision|
Teaching Rationale And Relationship
Lectures provide core material and guidance for further reading, problem solving practice is integrated into lecture structure.
The format of resits will be determined by the Board of Examiners
|Written Examination||120||2||A||100||Candidates to answer three long questions from a choice of four.|
|Module Code||Module Title||Semester||Comment|
|EEE8028||Image Processing and Machine Vision||2||N/A|
|EEE8098||Image Processing and Computer Vision||2||N/A|
Assessment Rationale And Relationship
Lectures provide the core material as well as guidance for further reading. Problem practice is integrated into lecture structure, practice is given in the use of the Matlab image processing toolbox.
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.