VISUAL INFORMATION SYSTEMS

CSE4VIS

2015

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 4 - UG/Hons/1st Yr PG

Exchange StudentsYes

Subject particulars

Subject rules

Prerequisites CSE2ICE or equivalent AND Enrolment in one of the following courses: SMIT, SMICT, SMCSC, SMBBS, SGBBS, SGIT or SGCS.

Co-requisitesN/A

Incompatible subjects CSE3VIS AND Students in the following courses are not permitted to enrol: SBCS, SBIT, SBCSGT, SVCSE, SZCSC, SBITP and SBBIY.

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. Lecture topic 1 on the introduction of visual information systems.
Related graduate capabilities and elements:
Discipline-specific GCs (Discipline-specific GCs)
Writing (Writing)

02. Evaluate the major issues in visual data 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 modelling, image database management and visual information retrieval in the exam papers. Lectures 2, 3, 4, 5, 6, 7 are on visual data representation, features and similarity, Facial Image Recognition, retrieval systems fundamentals.
Related graduate capabilities and elements:
Inquiry/ Research (Inquiry/ Research)
Discipline-specific GCs (Discipline-specific GCs)
Creative Problem-solving (Creative Problem-solving)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Critical Thinking (Critical Thinking)

03. Research and implementation of Facial Image Recognition (FIR) systems.

Activities:
One assignment on facial image recognition and 8 laboratories exercises, plus one group assignment on forensic image recognition. Lab 1 to Lab 8 are on FIR system implementation. MATLAB will be used to implement the FIR system.
Related graduate capabilities and elements:
Teamwork (Teamwork)
Creative Problem-solving (Creative Problem-solving)
Critical Thinking (Critical Thinking)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)

04. Evaluate the significance of image recognition techniques for real world applications.

Activities:
Students are required to complete some questions related to the specific techniques for image recognition and its applications in the exam papers. Lectures 8, 9, 10, 11 are on Recognition Systems Fundamentals, Feature-based Classifiers, Handwritten Numeral Recognition, and Face Image Recognition.
Related graduate capabilities and elements:
Discipline-specific GCs (Discipline-specific GCs)
Writing (Writing)
Critical Thinking (Critical Thinking)

Subject options

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Start date between: and    Key dates

Melbourne, 2015, Semester 1, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorJustin Wang

Class requirements

Laboratory Class
One 2.0 hours laboratory class per week and delivered via face-to-face.

Lecture
One 2.0 hours lecture per week and delivered via face-to-face.

Assessments

Assessment elementComments% ILO*
Assignment 2 - Forensic Image recognition techniques (Group project20 03
Assignment 1 - Facial image recognition system design20 03
Exam60 01, 02, 04