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Image Processing and Pattern Recognition
University of Technology Sydney - Direct Enroll Program
Sydney, Australia
Dates: 7/16/25 - 11/23/25
Image Processing and Pattern Recognition
OVERVIEW
CEA CAPA Partner Institution: University of Technology Sydney
Location: Sydney, Australia
Primary Subject Area: Computer Sciences
Instruction in: English
Course Code: 31256
Transcript Source: Partner Institution
Course Details: Level 300
Recommended Semester Credits: 4
Prerequisites: ( 48024 Programming 2 OR ((41039 Programming 1 OR 48023 Programming Fundamentals) AND 41085 Fundamentals Studio B))
DESCRIPTION
Images and videos contain enormous amounts of information that can be extracted automatically by means of image processing and pattern recognition techniques. The extracted information is at the basis of many innovative applications such as video surveillance, diagnosis from medical images, automatic indexing and retrieval of multimedia data, human-computer interaction. This subject gives students the ability to understand the principles of image processing and pattern recognition and develop software for the automatic analysis and interpretation of images and videos.
The goal of this subject is to teach skills used by professional engineers working at developing image processing and computer vision products, services and solutions. During the project students apply the knowledge they have gained to scope, solve, test and communicate a solution to a real-world image processing problem in a collaborative team-based environment. Examples include detection of people and objects in video surveillance, automated diagnosis from medical images, and detection and recognition of faces in imagery.
The goal of this subject is to teach skills used by professional engineers working at developing image processing and computer vision products, services and solutions. During the project students apply the knowledge they have gained to scope, solve, test and communicate a solution to a real-world image processing problem in a collaborative team-based environment. Examples include detection of people and objects in video surveillance, automated diagnosis from medical images, and detection and recognition of faces in imagery.