cse3vis visual information systems
VISUAL INFORMATION SYSTEMS
CSE3VIS
2017
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 subjects CSE31MS, CSE32MS, CSE41FMS, CSE42FMS, CSE3MS, CSE4FMS, CSE3IMS.
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
- Related graduate capabilities and elements:
- Literacies and Communication Skills(Writing,Quantitative Literacy)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
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
- Related graduate capabilities and elements:
- Literacies and Communication Skills(Writing,Quantitative Literacy)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Personal and Professional Skills(Study and Learning Skills)
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
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
- Related graduate capabilities and elements:
- Literacies and Communication Skills(Writing,Quantitative Literacy)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Personal and Professional Skills(Study and Learning Skills)
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
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
- Related graduate capabilities and elements:
- Literacies and Communication Skills(Writing,Quantitative Literacy)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving,Inquiry/Research)
- Personal and Professional Skills(Study and Learning Skills)
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
Subject options
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Melbourne, 2017, Semester 1, Day
Overview
Online enrolmentYes
Maximum enrolment sizeN/A
Enrolment information
Subject Instance Co-ordinatorJustin Wang
Class requirements
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.
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.
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 |