Note: This unit version is currently under review and is subject to change!

ELEC5304: Multidimensional Signal Processing (2019 - Semester 1)

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Unit: ELEC5304: Intelligent Visual Signal Understanding (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Postgraduate
Faculty/School: School of Electrical & Information Engineering
Unit Coordinator/s: Dr Ouyang, Wanli
Session options: Semester 1
Versions for this Unit:
Campus: Camperdown/Darlington
Pre-Requisites: None.
Brief Handbook Description: This unit of study introduces basic and advanced concepts and methodologies in image processing and computer vision. This course mainly focuses on image processing and analysis methods as well as intelligent systems for processing and understanding multidimensional signals such as images, which include basic topics like multidimensional signal processing fundamentals and advanced topics like visual feature extraction and image classification as well as their applications for face recognition and object/scene recognition. It mainly covers the following areas: multidimensional signal processing fundamentals, image enhancement in the spatial domain and frequency domain, edge processing and region processing, object recognition and detection.
Assumed Knowledge: Mathematics (e.g. probability and linear algebra) and programing skills (e.g. Matlab/Java/Python/C++)
Additional Notes: From 2020, this unit will be renamed to Intelligent Visual Signal Understanding.
Timetable: ELEC5304 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Lecture 2.00 1 13
2 Laboratory 1.00 1 11
3 Independent Study 5.00 13
T&L Activities: Independent Study: Students are expected to undertake the prescribed reading and work on homework exercises and assignments.

Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.

(7) Project and Team Skills (Level 3)
1. To be able to report results in a professional manner
(4) Design (Level 4)
2. To be able to use appropriate software platforms and tools for a given image processing or computer vision task
(3) Problem Solving and Inventiveness (Level 4)
3. To be able to use the existing image processing and computer vision packages
4. To be able to apply the image processing and computer vision techniques to solve real world applications
(1) Maths/ Science Methods and Tools (Level 3)
5. To be able to understand the fundamental theory of image processing and computer vision algorithms
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Final Exam No 70.00 Exam Period 3, 4, 5,
2 Project 1 No 15.00 Week 12 1, 2, 3, 4, 5,
3 Project 2 No 15.00 Week 12 1, 2, 3, 4, 5,
Assessment Description: Final Exam. Total weight: 70%

Project 1. Total weight: 15%

Project 2. Total weight: 15%

[1]Text-based similarity detecting software (Turnitin) will be used for all text-based written assignments.

[2]Late submission for lab reports: 1) There is no penalty for submissions until 11:59pm of the due day; 2) For submissions that are late than 11:59pm of the due day, 15% penalty will be applied for each day. Submissions that are late for one week will be given ZERO marks.
Grading:
Grade Type Description
Standards Based Assessment Final grades in this unit are awarded at levels of HD for High Distinction, DI (previously D) for Distinction, CR for Credit, PS (previously P) for Pass and FA (previously F) for Fail as defined by University of Sydney Assessment Policy. Details of the Assessment Policy are available on the Policies website at http://sydney.edu.au/policies . Standards for grades in individual assessment tasks and the summative method for obtaining a final mark in the unit will be set out in a marking guide supplied by the unit coordinator.
Policies & Procedures: See the policies page of the faculty website at http://sydney.edu.au/engineering/student-policies/ for information regarding university policies and local provisions and procedures within the Faculty of Engineering and Information Technologies.
Prescribed Text/s: Note: Students are expected to have a personal copy of all books listed.
  • Digital Image Processing
  • Pattern Classification
Recommended Reference/s: Note: References are provided for guidance purposes only. Students are advised to consult these books in the university library. Purchase is not required.
  • Computer Vision
  • Digital Image Processing Using MATLAB
  • Pattern Recognition and Machine Learning

Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar https://web.timetable.usyd.edu.au/calendar.jsp

Week Description
Week 1 Introduction / Multidimensional Signal Processing Fundamentals (Principles of camera systems for visual information acquisition and digitization)
Week 2 Mathematical Preliminaries for multi-dimensional signal processing -- 2D Convolution and Z-transform
Week 3 Mathematical Preliminaries for multi-dimensional signal processing -- Matrix Manipulation
Week 4 Mathematical Preliminaries for multi-dimensional signal processing -- deep learning
Week 5 Image Restoration
Week 6 Image Restoration - part 2
Week 7 Image Enhancement
Week 8 Image Analysis - Edge detection
Week 9 Image Analysis - Segmentation
Week 10 Image Analysis - Detection and recognition
Week 11 Application of deep learning for Multidimensional Signal Processing - part 1
Week 12 Application of deep learning for Multidimensional Signal Processing - part 2
Assessment Due: Project 1
Assessment Due: Project 2
Week 13 Review and preparation for the exam
Exam Period Assessment Due: Final Exam

Course Relations

The following is a list of courses which have added this Unit to their structure.

Course Year(s) Offered
Electrical Mid-Year 2016, 2017, 2018, 2019, 2020
Electrical/ Project Management 2019, 2020
Electrical 2016, 2017, 2018, 2019, 2020
Electrical / Arts 2016, 2017, 2018, 2019, 2020
Electrical / Commerce 2016, 2017, 2018, 2019, 2020
Electrical / Medical Science 2016, 2017
Electrical / Music Studies 2016, 2017
Electrical / Project Management 2016, 2017, 2018, 2020
Electrical / Science 2016, 2017, 2018, 2019, 2020
Electrical/Science (Health) 2018, 2019, 2020
Electrical / Law 2016, 2017, 2018, 2019, 2020
Software Mid-Year 2017
Software/ Project Management 2019, 2020
Software 2017
Electrical/Science (Medical Science Stream) 2018, 2019, 2020
Master of Engineering 2017, 2018, 2019, 2020
Master of Professional Engineering (Accelerated) (Electrical) 2019, 2020
Master of Professional Engineering (Accelerated) (Intelligent Information Engineering) 2020
Master of Professional Engineering (Electrical) 2017, 2018, 2019, 2020
Master of Professional Engineering (Intelligent Information Engineering) 2020

Course Goals

This unit contributes to the achievement of the following course goals:

Attribute Practiced Assessed
(6) Communication and Inquiry/ Research (Level 4) No 0%
(7) Project and Team Skills (Level 3) No 6%
(5) Interdisciplinary, Inclusiveness, Influence (Level 4) No 0%
(4) Design (Level 4) No 6%
(2) Engineering/ IT Specialisation (Level 4) No 0%
(3) Problem Solving and Inventiveness (Level 4) No 47%
(1) Maths/ Science Methods and Tools (Level 3) No 41%

These goals are selected from Engineering & IT Graduate Outcomes Table 2018 which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 2018 for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.