42177 Image Processing and Pattern Recognition
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Credit points: 6 cp
Result type: Grade and marks
Requisite(s): 32555 Fundamentals of Software Development
These requisites may not apply to students in certain courses. See access conditions.
Anti-requisite(s): 31256 Image Processing and Pattern Recognition
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, to develop software for the automatic analysis and interpretation of images and videos, and to research recent advances in the field.
The goal of this subject is to build advanced knowledge and skills required to develop 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. Students also engage with recent image processing research to develop a more advanced understanding of this rapidly evolving field.
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. | Conduct collaborative research into image processing and pattern recognition techniques in a professional context. (E.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)
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)
- Image and video representation
- Image processing, segmentation and analysis
- Matching in 2D
- Feature detection
- Statistical pattern recognition
- Video analysis
Assessment
Assessment task 1: Project Requirements and Specifications
| Intent: | To determine requirements and specifications on a project in the field of image processing and pattern recognition. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 3 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1, D.1 and E.1 |
| Type: | Essay |
| Groupwork: | Group, individually assessed |
| Weight: | 25% |
| Length: | 4500 words |
Assessment task 2: Project Implementation
| Intent: | To implement a professional project in the field of image processing and pattern recognition. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3 and 4 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: | 4500 words |
Assessment task 3: Project Research Presentation
| Intent: | To critically examine state of the art research in the field of image processing and pattern recognition. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 2, 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: | Individual |
| Weight: | 25% |
| Length: | 15 minutes |
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:
Shapiro, L. G., & Stockman, G. C. (2001). Computer vision. Prentice Hall.
Recommended texts
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:
Bishop, C. M. (2007). Pattern recognition and machine learning. Springer.
Gonzalez, R. C., & Woods, R. E. (2008). Digital image processing (3rd ed.). Prentice Hall.
Hartley, R., & Zisserman, A. (2003). Multiple view geometry in computer vision (2nd ed.). Cambridge University Press.
Forsyth, D., & Ponce, J. (2012). Computer vision: a modern approach (2nd ed.). Pearson.