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

Readings

Computer Vision Algorithms and Applications

Resource TypeRecommended

Resource RequirementN/A

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

01. Explain data sampling and quantization in the acquisition, colour vision and camera models.
02. Practice image processing techniques for basic computer vision tasks and address design issues.
03. Implement and analyse statistical and machine learning models for advanced computer vision tasks and address design issues.
04. Design, implement and evaluate a machine vision model to recognise visual concepts from images.

Subject options

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Start date between: and    Key dates

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 h 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 h lecture per week on weekdays during the day from week 30 to week 42 and delivered via face-to-face.

Assessments

Assessment elementCommentsCategoryContributionHurdle% ILO*

Design and implementation of a machine learning model for a computer vision task (equivalent to 3500

N/AN/AN/ANo50 SILO3, SILO4

One 2-hour examination(equivalent to 2000 words)

N/AN/AN/ANo50 SILO1, SILO2, SILO3, SILO4