IMAGE PROCESSING

CSE3VIS

2021

Credit points: 15

Subject outline

This subject covers both fundamentals of image processing as well as computing techniques with applications in many cutting-edge domains such as image recognition, object detection and segmentation, image registration and retrieval. Design issues on image recognition will be addressed, which contain eigenface technology, image feature extraction, similarity measurement, and performance evaluation. Practice on image recognition will be offered in Labs.

SchoolEngineering and Mathematical Sciences

Credit points15

Subject Co-ordinatorLydia Cui

Available to Study Abroad/Exchange StudentsYes

Subject year levelYear Level 3 - UG

Available as ElectiveNo

Learning ActivitiesN/A

Capstone subjectNo

Subject particulars

Subject rules

PrerequisitesCSE2AIF

Co-requisitesN/A

Incompatible subjectsN/A

Equivalent subjectsN/A

Quota Management StrategyN/A

Quota-conditions or rulesN/A

Special conditionsN/A

Minimum credit point requirementN/A

Assumed knowledgeN/A

Readings

Digital Image Processing

Resource TypeRecommended

Resource RequirementN/A

AuthorRafael C. Gonzalez, Richard E. Woods.

Year2017

Edition/VolumeN/A

PublisherPearson

ISBNN/A

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Pattern Recognition and Machine Learning

Resource TypeRecommended

Resource RequirementN/A

AuthorChristopher M. Bishop

Year2006

Edition/VolumeN/A

PublisherSpringer

ISBNN/A

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Career Ready

Career-focusedNo

Work-based learningNo

Self sourced or Uni sourcedN/A

Entire subject or partial subjectN/A

Total hours/days requiredN/A

Location of WBL activity (region)N/A

WBL addtional requirementsN/A

Graduate capabilities & intended learning outcomes

Graduate Capabilities

Intended Learning Outcomes

01. Demonstrate understanding of image processing fundamentals by being able to explain data sampling and quantization in acquisition, image enhancement and denoising, and image representation.
02. Define learned image processing and pattern recognition techniques for image recognition, object detection and segmentation, image registration and retrieval.
03. Implement an image recognition system with learned knowledge and techniques.
04. Evaluate the performance and robustness of image processing techniques for real world applications.

Subject options

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Start date between: and    Key dates

Melbourne (Bundoora), 2021, Semester 1, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorLydia Cui

Class requirements

Laboratory ClassWeek: 11 - 22
One 2.00 h 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 2.00 h lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Assessments

Assessment elementCommentsCategoryContributionHurdle% ILO*

Final centrally scheduled exam (online quiz via LMS) (2 Hours) (equivalent to 2100 words)

N/ACentral examIndividualYes70 SILO1, SILO2

Design report (equivalent to 1200 words)

N/AReportIndividualNo30 SILO3, SILO4

On-Line, 2021, Semester 1, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorLydia Cui

Class requirements

Laboratory ClassWeek: 10 - 22
One 2.00 h laboratory class per week on weekdays during the day from week 10 to week 22 and delivered via online.

LectureWeek: 10 - 22
One 2.00 h lecture per week on weekdays during the day from week 10 to week 22 and delivered via online.

Assessments

Assessment elementCommentsCategoryContributionHurdle% ILO*

Assignment (equivalent to 2100 words)

N/AAssignmentIndividualNo50 SILO3, SILO4

Final centrally scheduled exam (online quiz via LMS) (2 Hours) (equivalent to 2100 words)

N/ACentral examIndividualYes50 SILO1, SILO2, SILO4

On-Line, 2021, Semester 2, Online

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorLydia Cui

Class requirements

Laboratory ClassWeek: 30 - 42
One 2.00 h laboratory class per week on weekdays during the day from week 30 to week 42 and delivered via online.

LectureWeek: 30 - 42
One 2.00 h lecture per week on weekdays during the day from week 30 to week 42 and delivered via online.

Assessments

Assessment elementCommentsCategoryContributionHurdle% ILO*

Assignment (equivalent to 2100 words)

N/AAssignmentIndividualNo50 SILO3, SILO4

Final centrally scheduled exam (online quiz via LMS) (2 Hours) (equivalent to 2100 words)

N/ACentral examIndividualYes50 SILO1, SILO2, SILO4