Note: This unit is an archived version! See Overview tab for delivered versions.
AMME4710: Computer Vision and Image Processing (2013 - Semester 2)
| Unit: | AMME4710: Computer Vision and Image Processing (6 CP) |
| Mode: | Normal-Day |
| On Offer: | Yes |
| Level: | Senior Advanced |
| Faculty/School: | School of Aerospace, Mechanical & Mechatronic Engineering |
| Unit Coordinator/s: |
Professor Nebot, Eduardo
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| Session options: | Semester 2 |
| Versions for this Unit: | |
| Site(s) for this Unit: |
http://www.acfr.usyd.edu.au/courses/amme4710/ |
| Campus: | Camperdown/Darlington |
| Pre-Requisites: | None. |
| Brief Handbook Description: | This unit of study introduces students to vision sensors, computer vision analysis and digital image processing. This course will cover the following areas: fundamental principles of vision sensors such as physics laws, radiometry, CMOS/CDD imager architectures, colour reconstruction; the design of physics-based models for vision such as reflectance models, photometric invariants, radiometric calibration. This course will also present algorithms for video/image analysis, transmission and scene interpretation. Topics such as image enhancement, restoration, stereo correspondence, pattern recognition, object segmentation and motion analysis will be covered. |
| Assumed Knowledge: | Recommended prerequisite MECH4720 Sensors and Signals or MECH4730 Computers in Real-Time Control and Instrumentation |
| Additional Notes: | Current Lectures: Dr. Thierry Peynot, [email protected] Dr. Shrihari Vasudevan, [email protected] |
| Lecturer/s: |
Peynot, Thierry
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| Tutor/s: | Christopher Brunner | |||||||||||||||
| Timetable: | AMME4710 Timetable | |||||||||||||||
| Time Commitment: |
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| T&L Activities: | Laboratory: Fridays 2-5 |
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 |
| Research and Inquire, Personal and intellectual autonomy, Communication. These graduate attribute will be developed through challenging labs and presentation of results |
Design (Level 4) |
| The student will be required to understand image processing algorithms for restoration, object recognition, filtering etc. | Engineering/IT Specialisation (Level 4) |
| The student will be required to report the outcomes of the assignments and present code with documentation in a very professional manner | 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.
Design (Level 4)| Assessment Methods: |
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| Assessment Description: |
Project throughout the semester. Total weight: 60% (Progress Report 10% + Presentation 20% + Final Report 30%) Lab Tutorial: 5 Evaluated Tutorials |
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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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| Online Course Content: | http://www.acfr.usyd.edu.au/courses/amme4710/ |
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 / Digital Image Fundamentals |
| Week 2 | Intensity Transformations and Spatial Filtering |
| Week 3 | Filtering in the Frequency Domain |
| Week 4 | Colour Image Processing |
| Week 5 | Image Features |
| Week 6 | Segmentation |
| Week 7 | Image Stitching |
| Assessment Due: Project Progress Report | |
| Week 8 | Image Restoration |
| Week 9 | Recognition |
| Week 10 | Graphical Models |
| Week 11 | Recognition (cont.) |
| Week 12 | Introduction to stereo-vision |
| Assessment Due: Project Presentation | |
| Week 13 | Motion Estimation |
| Assessment Due: Project Final Report | |
| STUVAC (Week 14) | N/A |
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 |
| Design (Level 4) | Yes | 0% |
| Engineering/IT Specialisation (Level 4) | Yes | 80% |
| Communication (Level 4) | Yes | 18% |
| Project and Team Skills (Level 3) | No | 2% |
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.
