COMPUTER VISION
CSE5CV
2021
Credit points: 15
Subject outline
Computer vision is an interdisciplinary scientific field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. In this subject, you will be introduced to topics in computer vision, covering from early vision to mid and high-level vision such as camera imaging geometry, feature detection and matching, stereo, motion estimation and tracking, scene understanding and image captioning. You will practice statistical models and machine learning models for various computer vision tasks. You will have the opportunity to implement algorithms for real-world computer vision applications in labs.
SchoolEngineering and Mathematical Sciences
Credit points15
Subject Co-ordinatorLydia Cui
Available to Study Abroad/Exchange StudentsYes
Subject year levelYear Level 5 - Masters
Available as ElectiveNo
Learning ActivitiesN/A
Capstone subjectNo
Subject particulars
Subject rules
PrerequisitesCSE4IP
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
Learning resources
Computer Vision Algorithms and Applications
Resource TypeBook
Resource RequirementRecommended
AuthorRichard Szeliski
Year2011
Edition/VolumeN/A
PublisherSpringer-Verlag London
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
Subject options
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Melbourne (Bundoora), 2021, Semester 2, Day
Overview
Online enrolmentYes
Maximum enrolment sizeN/A
Subject Instance Co-ordinatorLydia Cui
Class requirements
Computer LaboratoryWeek: 32 - 43
One 2.00 hours computer laboratory per week on weekdays during the day from week 32 to week 43 and delivered via face-to-face.
LectureWeek: 30 - 42
One 2.00 hours lecture per week on weekdays during the day from week 30 to week 42 and delivered via face-to-face.
Assessments
| Assessment element | Category | Contribution | Hurdle | % | ILO* |
|---|---|---|---|---|---|
Design and implementation of a machine learning model for a computer vision task (equivalent to 3500 | N/A | N/A | No | 50 | SILO3, SILO4 |
One 2-hour examination(equivalent to 2000 words) | N/A | N/A | No | 50 | SILO1, SILO2, SILO3, SILO4 |