Note: This unit is an archived version! See Overview tab for delivered versions.
ELEC5304: Multidimensional Signal Processing (2017 - Semester 1)
| Unit: | ELEC5304: Intelligent Visual Signal Understanding (6 CP) |
| Mode: | Normal-Day |
| On Offer: | Yes |
| Level: | Postgraduate |
| Faculty/School: | School of Electrical & Computer Engineering |
| Unit Coordinator/s: |
Prof Xu, Dong
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| 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, imaging geometry and 3D stereo vision, object recognition and face recognition. |
| Assumed Knowledge: | Mathematics (e.g. probability and linear algebra) and programing skills (e.g. Matlab/Java/Python/C++) |
| Timetable: | ELEC5304 Timetable | ||||||||||||||||||||
| Time Commitment: |
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| T&L Activities: | Independent Study: Students are expected to undertake the prescribed reading and work on homework exercises and assignments. |
Attributes listed here represent the key course goals (see Course Map tab) designated for this unit. The list below describes how these attributes are developed through practice in the unit. See Learning Outcomes and Assessment tabs for details of how these attributes are assessed.
| Attribute Development Method | Attribute Developed |
| Students will gain practical experience in designing and implementing computer programs for solving image processing and computer vision tasks through challenging labs. | Design (Level 4) |
| The student will learn fundamental and advanced methods in image processing and computer vision as well as know how to use these methods to solve practical problems. | Engineering/IT Specialisation (Level 4) |
| The student will learn the fundamental theory of image processing and computer vision algorithms. | Maths/Science Methods and Tools (Level 3) |
| The student will be required to report the outcomes of the individual/group project and present code with documentation. | Communication (Level 4) |
For explanation of attributes and levels see Engineering & IT Graduate Outcomes Table.
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.
Engineering/IT Specialisation (Level 4)| Assessment Methods: |
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| 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. |
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| Grading: |
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| 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.
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| 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.
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Course Relations
The following is a list of courses which have added this Unit to their structure.
Course Goals
This unit contributes to the achievement of the following course goals:
| Attribute | Practiced | Assessed |
| Engineering/IT Specialisation (Level 4) | Yes | 47% |
| Maths/Science Methods and Tools (Level 3) | Yes | 41% |
| Design (Level 4) | Yes | 6% |
| Communication (Level 4) | Yes | 6% |
| Project and Team Skills (Level 3) | No | 0% |
These goals are selected from Engineering & IT Graduate Outcomes Table which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 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.
