31256 Image Processing and Pattern Recognition
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Subject handbook information prior to 2025 is available in the Archives.
Credit points: 6 cp
Subject level:
Undergraduate
Result type: Grade and marksRequisite(s): ( 48024 Programming 2 OR ((41039 Programming 1 OR 48023 Programming Fundamentals) AND 41085 Fundamentals Studio B))
Description
Images and videos contain enormous amounts of information that can be extracted automatically by means of image processing and pattern recognition techniques. The extracted information is at the basis of many innovative applications such as video surveillance, diagnosis from medical images, automatic indexing and retrieval of multimedia data, human-computer interaction. This subject gives students the ability to understand the principles of image processing and pattern recognition and develop software for the automatic analysis and interpretation of images and videos.
The goal of this subject is to teach skills used by professional engineers working at developing image processing and computer vision products, services and solutions. During the project students apply the knowledge they have gained to scope, solve, test and communicate a solution to a real-world image processing problem in a collaborative team-based environment. Examples include detection of people and objects in video surveillance, automated diagnosis from medical images, and detection and recognition of faces in imagery.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Use foundational techniques of image processing and analysis such as filtering, segmentation and local features to solve image processing problems of real world application. (C.1) |
|---|---|
| 2. | Build a statistical classifier and know how to use other classifiers. (D.1) |
| 3. | Apply image processing and pattern recognition techniques to detect objects and activities in images and video. (D.1) |
| 4. | Collaborate with team members to successfully complete a project. (E.1) |
| 5. | Utilise Matlab to develop scripts in these areas. (D.1) |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):
- Design Oriented: FEIT graduates apply problem solving, design and decision-making methodologies to develop components, systems and processes to meet specified requirements. (C.1)
- Technically Proficient: FEIT graduates apply abstraction, mathematics and discipline fundamentals, software, tools and techniques to evaluate, implement and operate systems. (D.1)
- Collaborative and Communicative: FEIT graduates work as an effective member or leader of diverse teams, communicating effectively and operating within cross-disciplinary and cross-cultural contexts in the workplace. (E.1)
Contribution to the development of graduate attributes
Engineers Australia Stage 1 Competencies
This subject contributes to the development of the following Engineers Australia Stage 1 Competencies:
- 1.1. Comprehensive, theory based understanding of the underpinning natural and physical sciences and the engineering fundamentals applicable to 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.
- 3.6. Effective team membership and team leadership.
Teaching and learning strategies
Formal and informal, in-class and out-of-class engagement by students constitutes a large part of the learning activities that are covered in this subject.
Students should prepare before every class by accessing the weekly preparation material in Canvas that will generally consist of short videos and guided questions on topics related to the subject matter presented in that class.
In each session students will discuss the preparation material and then more in-depth content will be presented. Together with the lecture material, students will do exercises using MATLAB to practice their understanding of the concepts with verbal feedback from the tutors.
These exercises can be done individually or in small groups. For the next 5 weeks of the subject, the students will be working on a group project. The students will group together in small teams and apply the knowledge they have gained in the course of the previous classes to solve a real world image processing problem. Students are expected to apply learnt content, problem solving and research skills to write a specification of the problem they are attempting to solve, implement the solution and present the solution to the class. To complete this task successfully, students will need to work effectively in a team as well as research the problem individually and in a group outside of the class and bring that knowledge to the sessions.
A formative feedback quiz will be given in class during week 4 with feedback on correct answers and a worked solution given by the tutor to help students assess their understanding of the subject content. Feedback from the lecturer will also be given at various points during the group project. Firstly, written feedback will be given on the submitted design specification for the project, this feedback can help students with the second assessment task. Secondly, verbal feedback from both the lecturer and tutors during the project sessions to help students gauge how they are progressing. Additionally, there will be a point mid-way through the project where students will provide formative peer feedback on group contribution.
Content (topics)
1. Image and video representation
2. Image processing, segmentation and analysis
3. Matching in 2D
4. Feature detection
5. Statistical pattern recognition
6. Video analysis
Assessment
Assessment task 1: Project Requirements and Specifications
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2 and 3 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1 and D.1 |
|---|---|
| Type: | Essay |
| Groupwork: | Group, individually assessed |
| Weight: | 25% |
| Length: | 4,500 words |
Assessment task 2: Project Implementation
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3, 4 and 5 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1, D.1 and E.1 |
|---|---|
| Type: | Project |
| Groupwork: | Group, individually assessed |
| Weight: | 50% |
| Length: | 4,500 words |
Assessment task 3: Project Presentation
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 3 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): D.1 and E.1 |
|---|---|
| Type: | Presentation |
| Groupwork: | Group, individually assessed |
| Weight: | 25% |
Minimum requirements
In order to pass the subject, a student must achieve an overall mark of 50% or more.
Required texts
There is no single textbook in this subject. The book that is most heavily referenced is: Linda G. Shapiro, George C. Stockman, Computer Vision, Prentice Hall.
References
For the various topics covered by this subject, students can make reference to a number of excellent books in the broader areas of image processing, image analysis, computer vision, and pattern recognition:
Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer
Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing, 3/e, Prentice Hall
Richard Hartley, Andrew Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press
David A. Forsyth, Jean Ponce, Computer Vision: A Modern Approach, Prentice Hall
Other resources
Online support is on the subject's website on Canvas: https://canvas.uts.edu.au/ (this subject runs at 'Level 2': the website makes available subject information, materials, gradebook and an unmoderated discussion board).