EGH444 Digital Signals and Image Processing


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Unit Outline: Semester 2 2024, Gardens Point, Internal

Unit code:EGH444
Credit points:12
Pre-requisite:EGB342 or Admission to (EN50, EN55 or EN60)
Assumed Knowledge:

Linear Algebra, Basic probability and statistics, Differential and integral calculus, complex numbers and exponential representations are assumed knowledge

Coordinator:Maryam Haghighat | maryam.haghighat@qut.edu.au
Disclaimer - Offer of some units is subject to viability, and information in these Unit Outlines is subject to change prior to commencement of the teaching period.

Overview

This unit covers fundamentals of digital signal and image processing, including image representation and
acquisition, filtering (in both spatial and frequency domains), image enhancement. It will also introduce you to more
advanced concept such as feature extraction, segmentation, compression and machine learning applied to computer
vision.

You will learn how those techniques work and how and when to apply them. You will practice these concepts
individually and in collaboration with peers.

You will draw on the fundamentals of signals seen in EGB342.

Learning Outcomes

On successful completion of this unit you will be able to:

  1. Interpret, classify, compare and relate digital signal and image processing methods, at a developed level
  2. Interpret, explain, and critically refl ect on image processing problems and solutions such as image analysis, formation, measurement, recognition, detection and classification, at a developed level
  3. Take apart, investigate and solve contextualised practical detection and feature extraction problems using abstraction and interpretation methods, at a developed level
  4. Collaborate to solve complex digital image processing problems, at a developed level

Content

1. Overview of digital image representation, image formation and image processing systems.
2. Signal and Image enhancement, including fundamentals of 2D signals, filtering in the spatial and frequency
domains.
3. Image compression
4. Image features and feature extraction
5. Segmentation and classification
6. Machine learning (including deep learning) for computer vision applications

Learning Approaches

In this unit you can expect to experience the following timetabled activities:

  • Formal lectures with experienced engineers to give you insight into professional engineering knowledge, skills
    and attributes. In these lectures the key concepts and principles will be introduced, explained and illustrated.
  • Weekly practical computer lab sessions where you will be applying the concepts introduced in the lectures to
    problem-solving tasks, and practice coding exercises on authentic tasks. In these practical session you will
    apply principles of programming to implement fundamental image processing, computer vision, and deep
    learning algorithms, and explore digital signal and image processing concepts with tutors and other students.

To complement timetabled activities you will be provided with:

You are expected to perform the following activities:

  • Weekly problem-solving tasks, which you are expected to attempt on your own, ahead of the computer lab
    sessions.
  • Assessed tasks, to evaluate your ability to solve authentic digital signal and image processing problems, and
    provide feedback on your understanding and skill development.
  • Group project to further refine your technical skill development and examine problem-solving strategies on a
    larger task, in collaboration with peers.

Feedback on Learning and Assessment

Each assessment submission will be marked against criteria and standards which will be shared with you at the beginning of semester through assessment task descriptions and marking rubrics. Marked assessment will include feedback from markers, against the criteria.
Formative feedback will be provided throughout the class discussions and practical computer lab sessions. Summative and formative feedback will be provided on the main areas targeted by the assessments.

Assessment

Overview

Assessment in this unit has been designed to give you the opportunity to show your learning against the unit learning outcomes.
It will measure your acquisition of the important concepts and your ability to apply and implement theoretical developments to contextualised digital signal and image processing problems. You will be working individually and in small groups solving problems using a variety of analytical and computer-based techniques, such as the use of Matlab/Python. Wherever possible this unit will utilise a project based approach to assignment design.

Unit Grading Scheme

7- point scale

Assessment Tasks

Assessment: Group Mini Project

You will get to conduct a small project-based assignment that will involve, software development, implementing image processing and computer vision techniques to solve a real-world, experimentation, results analysis and presentation.

Weight: 30
Individual/Group: Group
Due (indicative): Week 13
Related Unit learning outcomes: 2, 3, 4
Related Standards: EASTG1CMP: 1, 1.2, 1.3, 1.4, 2, 2.1, 2.3, 2.4, 3, 3.2, 3.6

Assessment: Practical Tasks

You will investigate solutions to specific practical tasks and problems, compare methods and results, justify the selection of specific approaches, present and analyse results.

Weight: 30
Individual/Group: Individual
Due (indicative): During semester
Related Unit learning outcomes: 1, 2
Related Standards: EASTG1CMP: 2, 2.3, 3, 3.2, 3.3, 3.5

Assessment: Final Exam

Invigilated assessment focusing on problem solving

 

Relates to learning outcomes
1, 3, 4

On Campus invigilated Exam. If campus access is restricted at the time of the central examination period/due date, an alternative, which may be a timed online assessment, will be offered. Individual students whose circumstances prevent their attendance on campus will be provided with an alternative assessment approach.

Weight: 40
Individual/Group: Individual
Due (indicative): Central Examination Period
Central exam duration: 2:10 - Including 10 minute perusal
Related Unit learning outcomes: 1, 2, 3
Related Standards: EASTG1CMP: 1, 1.3, 1.4, 2, 2.1

Academic Integrity

Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.

Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.

You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.

Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.

Resources

There are numerous suitable reference texts for this unit, many of which can be located in the library. You should also make use of suitable online resources such as video instructions for specific problems. Additional resources, including audio and video recordings of appropriate segments of the lectures will be made available via the QUT Canvas site for this unit.

Resource Materials

Recommended text(s)

R. Gonzalez and R. Woods. Digital Image Processing. 4th Edition. Pearson. 2017.

Risk Assessment Statement

There are no out of the ordinary risks associated with this unit, as all classes will be held in lecture theatres and small group tutorial rooms. Emergency exits and assembly areas will be made apparent to all attending students. You are referred to the University policy on health and safety (http://www.mopp.qut.edu.au/A/A_09_01.jsp)

Standards/Competencies

This unit is designed to support your development of the following standards\competencies.

Engineers Australia Stage 1 Competency Standard for Professional Engineer

1: Knowledge and Skill Base


  1. Relates to: Group Mini Project

  2. Relates to: Group Mini Project, Final Exam

  3. Relates to: Group Mini Project, Final Exam

2: Engineering Application Ability


  1. Relates to: Group Mini Project, Final Exam

  2. Relates to: Group Mini Project, Practical Tasks

  3. Relates to: Group Mini Project

3: Professional and Personal Attributes


  1. Relates to: Group Mini Project, Practical Tasks

  2. Relates to: Practical Tasks

  3. Relates to: Practical Tasks

  4. Relates to: Group Mini Project

Course Learning Outcomes

This unit is designed to support your development of the following course/study area learning outcomes.

EN01 Bachelor of Engineering (Honours)

  1. Engage stakeholders professionally and communicate the outcomes of your work effectively to expert and non-expert audiences using appropriate modes.
    Relates to: Group Mini Project, Practical Tasks, Final Exam
  2. Display leadership, creativity, and initiative in both self-directed and collaborative contexts of professional engineering practice.
    Relates to: Practical Tasks, Final Exam
  3. Demonstrate coherent knowledge and skills of physical, mathematical, statistical, computer, and information sciences that are fundamental to professional engineering practice.
    Relates to: Group Mini Project
  4. Demonstrate a thorough understanding of one engineering discipline, its research directions, and its application in contemporary professional engineering practice.
    Relates to: Group Mini Project, Final Exam

EN55 Master of Professional Engineering

  1. Apply advanced and specialist knowledge, concepts and practices in engineering design, analysis management and sustainability.
    Relates to: Practical Tasks, Final Exam
  2. Critically analyse and evaluate complex engineering problems to achieve research informed solutions.
    Relates to: Group Mini Project, Practical Tasks, Final Exam
  3. Communicate complex information effectively and succinctly, presenting high level reports, arguments and justifications in oral, written and visual forms to professional and non specialist audiences.
    Relates to: Group Mini Project, Practical Tasks
  4. Organise and manage time, tasks and projects independently, and collaboratively demonstrating the values and principles that shape engineering decision making and professional accountability.
    Relates to: Group Mini Project

EN60 Graduate Certificate in Communication for Engineering

  1. Demonstrate and apply specialised knowledge and technical skills in at least one Engineering discipline.
    Relates to: Group Mini Project, Final Exam
  2. Critically investigate real world engineering issues and solve complex problems drawing on specialised creative skills, analysis, evaluation and synthesis of discipline knowledge, theory and practice.
    Relates to: Practical Tasks, Final Exam
  3. Employ effective written and oral professional communication skills across social, cultural and discipline domains.
    Relates to: Group Mini Project, Practical Tasks
  4. Exercise responsibility and accountability in applying knowledge and skills for own learning and effective practice including working independently, ethically and collaboratively.
    Relates to: Group Mini Project, Practical Tasks

EV01 Bachelor of Engineering (Honours)

  1. Engage stakeholders professionally and communicate the outcomes of your work effectively to expert and non-expert audiences using appropriate modes.
    Relates to: Group Mini Project, Practical Tasks, Final Exam
  2. Display leadership, creativity, and initiative in both self-directed and collaborative contexts of professional engineering practice.
    Relates to: Practical Tasks, Final Exam
  3. Demonstrate coherent knowledge and skills of physical, mathematical, statistical, computer, and information sciences that are fundamental to professional engineering practice.
    Relates to: Group Mini Project
  4. Demonstrate a thorough understanding of one engineering discipline, its research directions, and its application in contemporary professional engineering practice.
    Relates to: Group Mini Project, Final Exam