FIT3081 Image processing Monash University

FIT3081 – Image processing
This unit introduces fundamental image processing techniques for the digital manipulation of 2D image data. Algorithms explored include those for edge detection, image enhancement, feature and shape extraction, segmentation and noise removal. The unit provides students an opportunity to develop theoretical understanding of these algorithms, and practical skills in implementing and applying them to real image data.
Faculty of Information Technology
Study level:
Undergraduate
Open to exchange or study abroad students?
Owning organisational unit:
Faculty of Information Technology
Credit points:

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Requisites Prerequisite
 FIT1008 6 CP Introduction to computer science
 FIT2085 6 CP Introduction to computer science for engineers
Offerings S1-01-MALAYSIA-ON-CAMPUS
Location: Malaysia
Teaching period: First semester Attendance mode: On-campus
Chief Examiner(s)
Professor Raveendran Paramesran
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This unit is only available for students enrolled in Monash Malaysia.
Learning outcomes
On successful completion of this unit, you should be able to:
1. explain the type of algorithms required for a particular image processing task among a wide range of available methodologies
2. develop programs for manipulating grey level and colour images using standard image processing algorithms;
3. develop and analyse algorithms for image enhancement, edge detection, image segmentation, image classification, machine learning and computer vision;
4. develop algorithms to extract and analyse features in large databases of medical, document, and other images
5. participate in a team as an image processing specialist communicating with other team members to develop image processing software.
Offering(s):
Applies to all offerings
Teaching approach Problem-based learning

Assessment
Assessment 1 Value %: 15
Assessment 2 Value %: 35
Assessment 3 Value %: 20
Final Take-home assessment Value %: 30
Scheduled teaching activities Workshops
Total hours: 48 hours Offerings:
Applies to all offerings
Active learning

Workload requirements Workload
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.
Learning resources Required resources
Prescribed text(s)
Limited copies of prescribed texts are available for you to borrow in the library.
1. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Pearson; 4th edition, 2017
2. R. C. Gonzalez and R. E. Woods, Digital Image Processing Using Matlab, 2nd Edition, 2009
Availability in areas of study
Advanced computer science

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