Module COMP52615: Computer Vision

Department: Computer Science

COMP52615: Computer Vision

Prerequisites

• None

Corequisites

• COMP52815 Robotics - Planning and Motion; COMP52715 Deep Learning for Computer Vision and Robotics; PHYS51915 Introduction to Machine Learning and Statistics; PHYS52015 Introduction to Scientific and High Performance Computing

Excluded Combination of Modules

• None

Aims

• Develop knowledge of key concepts, approaches and algorithms in Computer Vision related to automatic understanding of image and video data sources;
• Develop critical understanding and appreciation of current theoretical and empirical research in computer vision and its application within industry.

Content

• Themes will be chosen from contemporary areas of computer vision including the following:
• basic, intermediate and advanced features representations
• object detection and object/scene classification
• colour segmentation
• action recognition
• stereo vision
• optical flow
• object tracking
• super-resolution
• image and video summarization
• face and facial expression recognition

Learning Outcomes

• By the end of the module students should have:
• developed a critical understanding of the contemporary computer vision topics presented, how these are applicable to relevant industrial problems and have future potential for emerging needs in both a research and industrial setting;
• developed an advanced knowledge of the principles and practice of analysing relevant computer vision algorithms for problem suitability;
• developed a good understanding of managing the trade-off between task performance and real-time processing performance within the context of computer vision;
• explored the most recent advancements in the relevant academic literature and developed a critical understanding of their implications for current industry practice.

• By the end of the module, students should have developed highly specialised and advanced technical, professional and academic skills that enable them to:
• formulate and solve problems that involve the automatic understanding of image and video data sources using a range of algorithmic approaches;
• develop computer vision software solutions and use appropriate algorithms and approaches to address both industrial and research application tasks

• Written communication;
• Planning, organising and time management;
• Problem solving and analysis;
• Using initiative
• Adaptability
• Numeracy
• Computer literacy

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• A combination of lectures, seminars, and guided reading will contribute to achieving the aims and learning outcomes of this module.
• The summative written assignment will test students' knowledge and critical understanding of the material covered in the module, their analytical and problem-solving skills.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

Feedback on coursework.

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University