| CODE | ARI2129 | ||||||||||||
| TITLE | Principles of Computer Vision for AI | ||||||||||||
| UM LEVEL | 02 - Years 2, 3 in Modular Undergraduate Course | ||||||||||||
| MQF LEVEL | 5 | ||||||||||||
| ECTS CREDITS | 5 | ||||||||||||
| DEPARTMENT | Artificial Intelligence | ||||||||||||
| DESCRIPTION | Computer Vision is one of the key research areas of artificial intelligence. It is a multidisciplinary field that handles the key steps required to enable machines to reason about image and video data. This area is organised into three main stages, namely image acquisition, image processing and understanding. This study-unit serves as an introduction to this field by exposing the students to a high level understanding of every stage. The focus on AI will be present in this unit and students will therefore be empowered to comprehend how the understanding stage of this discipline is related to machine learning and other AI techniques being covered in other study-units. Study-Unit Aims: - Introduce the discipline of Computer Vision; - Enable students to familiarise themselves with the main stages of Computer Vision; - Provide students with introductory skills in Computer Vision; - Expose students to a variety of tools and frameworks that can empower them when working on Computer Vision projects; - Motivate students to venture in the stream of Computer Vision. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Describe the three stages of Computer Vision (Acquisition, Processing and Understanding); - Discuss and explore different approaches in image acquisition and processing; - Familiarise themselves with different types of image and video input data; - Explain how Computer Vision systems can be integrated with other AI systems; - Describe how the understanding stage in Computer Vision is a machine learning problem. 2. Skills: By the end of the study-unit the student will be able to: - Make use of tools and frameworks that enable Computer Vision; - Discuss and critically analyse approaches taken in the different stages of Computer Vision; - Identify scenarios where an Computer Vision system can make a difference in people’s lives; - Implement their own simple Computer Vision system. Main Text/s and any supplementary readings: Main Texts: - Tim Morris. Computer Vision and Image Processing. Palgrave Macmillan, 2004. - Computer Vision: Algorithms and Applications, by Rick Szeliski. A free electronic copy is available Online on http://szeliski.org/Book/ Supplementary Readings: - Thomas B. Moelsund. Introduction to Video and Image Processing. Springer, 2012. - Rafael C. Gonzalez and Richard E. Woods. Digital Image Processing (3rd or 4th Edition). Prentice-Hall, 2006. |
||||||||||||
| STUDY-UNIT TYPE | Lecture, Independent Study and Project | ||||||||||||
| METHOD OF ASSESSMENT |
|
||||||||||||
| LECTURER/S | Dylan Seychell |
||||||||||||
|
The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2024/5. It may be subject to change in subsequent years. |
|||||||||||||