Course Title: Image Processing

Part A: Course Overview

Course Title: Image Processing

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

EEET2169

City Campus

Undergraduate

125H Electrical & Computer Engineering

Face-to-Face

Sem 2 2007,
Sem 2 2008,
Sem 2 2009,
Sem 2 2010,
Sem 2 2011,
Sem 2 2012,
Sem 2 2013,
Sem 2 2014,
Sem 2 2015,
Sem 2 2016

EEET2169

City Campus

Undergraduate

172H School of Engineering

Face-to-Face

Sem 1 2017,
Sem 1 2018,
Sem 1 2019,
Sem 1 2020,
Sem 1 2021

Course Coordinator: Dr Shaun Cloherty

Course Coordinator Phone: +61 3 9925 0424

Course Coordinator Email: shaun.cloherty@rmit.edu.au

Course Coordinator Location: 12.08.17

Course Coordinator Availability: by appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

You will need basic knowledge of digital signal processing and proficiency in Matlab.

Knowledge of engineering mathematics including transform theory and matrix algebra is an advantage.

 


Course Description

Image Processing is an area of information science and engineering of growing importance with a wide range of applications, including: digital photography, video conferencing and streaming, industrial imaging and computer vision systems, autonomous vehicles, remote sensing and satellite imaging, video surveillance and security systems, and medical imaging such as CT (computed tomography), MRI (magnetic resonance imaging), PET (positron emission tomography) scan, mammography and ultrasound imaging.

This course will cover topics on digital image processing fundamentals, image analysis, image de-noising and restoration, signal compression, morphological image processing, image segmentation and description as well as contemporary applications of neural networks and machine learning in image analysis and computer vision.

The course will contribute to consolidation of students’ engineering mathematical knowledge and engineering programming skills, extension of theoretical knowledge and practical skills to solve multidimensional signal processing problems, and the development of your ability to work on multidisciplinary issues in diverse areas of digital imaging image processing, computer vision, and machine learning applications. 

The course will reinforce and enhance students’ skills in technical communication through preparation of technical laboratory reports and presentations.

Please note that if you take this course for a bachelor honours program, your overall mark in this course will be one of the course marks that will be used to calculate the weighted average mark (WAM) that will determine your award level. (This applies to students who commence enrolment in a bachelor honours program from 1 January 2016 onwards. See the WAM information web page for more information (www1.rmit.edu.au/browse;ID=eyj5c0mo77631).

 


Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes (PLOs): 

1.1.   Comprehensive, theory based understanding of the underpinning natural and physical sciences and the engineering fundamentals applicable to the engineering discipline

1.2.   Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline

1.3.   In-depth understanding of specialist bodies of knowledge within the engineering discipline

1.4.   Discernment of knowledge development and research directions within the engineering discipline

2.1.   Application of established engineering methods to complex engineering problem solving

2.2.   Fluent application of engineering techniques, tools and resources

2.3.   Application of systematic engineering synthesis and design processes

3.2 Effective oral and written communication in professional and lay domains 

3.5 Orderly management of self, and professional conduct.


On completion of this course, you should be able to (CLOs):

  1. Describe the basic issues and the scope (or principal applications) of image processing, and the roles of image processing and systems in a variety of applications
  2. Demonstrate a good understanding of the history and the current state-of-the-art image processing systems and applications which constantly push the boundaries and raise challenges in other fields of studies such as mathematics, physics, and computer systems engineering
  3. Identify areas of knowledge which are required, select an appropriate approach to a given image processing task, and critically evaluate and benchmark the performance of alternative techniques for a given problem
  4. Implement image processing tasks with a high level of proficiency via software
  5. Identify applications of image processing to advancement of knowledge in sciences and engineering with benefits in, e.g., policing, public safety and security, and social issues such as privacy
  6. Demonstrate a high level of self-directed learning ability and good oral and written communication skills on technical topics of image processing and systems engineering.

 


Overview of Learning Activities

Student Learning occurs through the following experiences and evaluation processes:

  • Recorded lectures where syllabus material will be presented and explained, and the subject will be illustrated with demonstrations and examples;
  • Completion of tutorial questions and laboratory projects which provide an introduction to software tools for design, simulation and evaluation of image processing systems, and are designed to give further practice in practical application of the course material and provide feedback on student progress and understanding;
  • Self-directed private study and problem-based learning, working through the course material as presented in class and learning materials, and gaining practice at solving conceptual and numerical problems.

Feedback will be provided throughout the semester in class and/or online discussions, through individual and group feedback on practical exercises and by individual consultation.

 


Overview of Learning Resources

You will be expected to use library and electronic resources (as well as any other appropriate resources) to engage in professional reading and private study of relevant material on image processing and machine learning.

The learning resources for this course include:

  • Lecture material prepared by the teaching staff.
  • Recommended textbook and references as listed in the Course Guide Part B and the RMIT online teaching platform.
  • You will be expected to have access to suitable computing equipment for design and evaluation of image processing systems. Required software (MATLAB) is freely available to RMIT students.


Overview of Assessment

☒This course has no hurdle requirements.

Assessment for this course consists of the following components:

  • Laboratory Tasks.
  • Laboratory Project.
  • Final Assessment (submitted online).

You will be required to submit formal individual reports for each laboratory task. Feedback will be provided in the submitted report, via Canvas, or via individual consultation. Furthermore, during the laboratory sessions the tutor will provide further insight into your work and how it could potentially be improved or expanded

Assessment tasks

Assessment Task 1: Laboratory Tasks

Weighting 50%

This assessment task supports CLOs 2, 3, 4, 5, & 6

Assessment Task 2: Laboratory Project

Weighting 35%

This assessment task supports CLOs 1, 2, 3, 4, 5, & 6

Assessment Task 3: Final Assessment

Weighting 15%

This assessment task supports CLOs 1, 2, 3, 5, & 6