CSE3VIS
VISUAL INFORMATION SYSTEMS
CSE3VIS
2018
Credit points: 15
Subject outline
This subject covers an overview of visual information access, image representation, feature extraction, image recognition and understanding, and content-based image retrieval techniques.Design issues on facial image recognition and content-based image retrieval systems for image database management will be addressed, which contain eigenface technology, image feature extraction, indexing, similarity measure, lower-bounding lemma and performance evaluation. Practice on facial image recognition (FIR) will be offered in Labs.
SchoolSchool Engineering&Mathematical Sciences
Credit points15
Subject Co-ordinatorJustin Wang
Available to Study Abroad StudentsYes
Subject year levelYear Level 3 - UG
Exchange StudentsYes
Subject particulars
Subject rules
Prerequisites CSE2AIF or CSE2DBF
Co-requisitesN/A
Incompatible subjectsN/A
Equivalent subjectsN/A
Special conditionsN/A
Learning resources
Readings
Resource Type | Title | Resource Requirement | Author and Year | Publisher |
---|---|---|---|---|
Readings | Content-based image and video retrieval | Recommended | Marques, O and Furht, B 2002 | 1ST ED, SPRINGER |
Readings | Image recognition and classification: algorithms, systems and applications | Recommended | Javidi, B 2002 | 1ST EN, CRC PRESS |
Graduate capabilities & intended learning outcomes
01. Define the technologies used in visual information systems
- Activities:
- Students are required to complete the questions related to the specific information and knowledge in the exam papers
02. Learn the major issues in face recognition, content-based image database management, and describe how to effectively represent visual data for visual information processing
- Activities:
- Students are required to complete all questions related to the specific techniques for visual data modeling, face recognition and visual information retrieval in the exam papers
03. Demonstrate hands-on experience in developing a face recognition (FR) system based on eigenface technology
- Activities:
- one assignment on face recognition system design, and 8 laboratories exercises
04. Analyse the robustness of face recognition systems
- Activities:
- Students are required to understand the significance of robust face image recognition for real world applications
Subject options
Select to view your study options…
Melbourne, 2018, Semester 1, Day
Overview
Online enrolmentYes
Maximum enrolment sizeN/A
Enrolment information
Subject Instance Co-ordinatorJustin Wang
Class requirements
LectureWeek: 10 - 22
One 2.0 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
Laboratory ClassWeek: 11 - 22
One 2.0 hours laboratory class per week on weekdays during the day from week 11 to week 22 and delivered via face-to-face.
Assessments
Assessment element | Comments | % | ILO* |
---|---|---|---|
one 3-hour examination | Hurdle requirement: To pass the subject, a pass in the examination is mandatory. | 70 | 01, 02 |
One design report (750 words) | The assignment is about face recognition system design | 30 | 03, 04 |