Module COMP53415: Computer Vision

Department: Computer Science

COMP53415: Computer Vision

Prerequisites

• None

Corequisites

• None

Excluded Combination of Modules

• None

Aims

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

Content

• Content will cover classical and deep learning approaches to Computer Vision and be chosen from:
• Comprehensive image feature representations
• Object detection, object classification and scene understanding
• Segmentation, superpixels, saliency, optical flow and image registration
• Stereo vision and reconstruction from multiple images
• Object tracking and behaviour analysis
• Real-time processing approaches and trade-offs
• Image transformation and augmentation
• Applications of computer vision

Learning Outcomes

• By the end of this module, students should be able to demonstrate:
• 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.
• knowledge of state-of-the-art deep learning models for computer vision tasks.
• advanced knowledge of the principles and practice of analysing computer vision algorithms to determine problem suitability.
• an understanding of managing the trade-off between task performance and real-time processing performance within the context of computer vision.
• an understanding of the most recent advancements in the relevant academic literature and their implications for current industry practice.

• By the end of this 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, computer classes, and guided reading will contribute to achieving the aims and learning outcomes of this module.
• The summative assessment will test students’ knowledge and critical understanding of the material covered in the module and their analytical and problem-solving skills.
• The assignment element of the coursework component consists of a coding exercise with accompanying report.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

Via computer classes.

■ 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