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

Readings

Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsContent-based image and video retrievalRecommendedMarques, O and Furht, B 20021ST ED, SPRINGER
ReadingsImage recognition and classification: algorithms, systems and applicationsRecommendedJavidi, B 20021ST 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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Start date between: and    Key dates

Melbourne, 2017, Semester 1, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorJustin Wang

Class requirements

Laboratory Class Week: 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.

Lecture Week: 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 elementComments% ILO*
one 3-hour examinationHurdle 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 design30 03, 04