Course Title: Image Processing

Part A: Course Overview

Course Title: Image Processing

Credit Points: 12.00


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

Course Coordinator: Professor Hong Ren Wu

Course Coordinator Phone: +61 3 9925 5376

Course Coordinator Email: henry.wu@rmit.edu.au

Course Coordinator Location: 10.10.06


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 video conferencing and smart phones, TV (television) broadcasting and video streaming, radar and infrared imaging, satellite imaging, digital photography, industrial imaging systems, video surveillance and security systems, multimedia computing and retrieval, and medical imaging such as CR (computed radiography), CT (computed tomography), MRI (magnetic resonance imaging), PET (positron emission tomography) scan, mammography and ultrasound imaging.

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 visual signal processing problems, and the development of your ability to work on multidisciplinary issues in diverse areas of digital imaging and image processing applications as aforementioned.

The course will reinforce and enhance students’ communication skills in technical writing by written assignments and laboratory work reports.

This course covers topics on digital image processing fundamentals, image analysis, image de-noising and restoration, visual signal compression, morphological image processing, image segmentation and description techniques, and data hiding and watermarking concepts and techniques.

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 of the Bachelor of Engineering (Honours):

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; and

2.3.   Application of systematic engineering synthesis and design processes.

 


On successful completion of the course, you should be able to:

  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 by simulation using, e.g., Matlab;
  4. Implement image processing tasks with a high level of proficiency via software and hardware systems;
  5. Identify potential 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; and
  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

Individual learning activities include attending lectures, contribution to class discussions, completing written assignments, attending and completing laboratory assignments on image processing tasks using Matlab numerical computing environment programming language, completing laboratory assignment reports, and self-directed study.

Laboratory practical work includes defined and optional image processing tasks to be carried out and demonstrated in laboratory sessions.

Written assignments include written assignment(s) and laboratory work reports in a format and style suitable for scientific and technical writing.

 


Overview of Learning Resources

Matlab numerical computing environment and programming language is provided for you to use during laboratory sessions and outside laboratory hours (when laboratory is not required for other classes).

Lecture notes and laboratory instructions will be provided on-line as well as information on textbook and references for further reading.

You may find it convenient to acquire and install student edition of Matlab on your home computer or laptop.

 


Overview of Assessment

☒This course has no hurdle requirements.

Your knowledge and ability to explain key concepts and to demonstrate proficiency in image processing tasks will be assessed through a written examination, laboratory practical work and reporting, and a written assignment.

Practical image processing skills will be assessed through laboratory work demonstration and written reports on laboratory exercises.

The written assignment will be assessed to evaluate your integrated knowledge and understanding on a defined image analysis and processing topic, and your analytical skills and your communication skills in technical writing.

All assessment tasks will also assess your ability to critically analyse results and provide arguments to support your work and findings. Feedback will be provided on all assessment tasks except for the Final exam.

Assessment tasks

Assessment Task 1: Four fortnightly laboratory practical work and reports

Weighting 48%

This assessment task supports CLOs 3 - 6

Assessment Task 2: Final exam

Weighting 40%

This assessment task supports CLOs 1-3,5-6

Assessment Task 3: Written assignment

Weighting 12%

This assessment task supports CLOs 3, 5 & 6