| CODE | ARI3129 | ||||||||||||||||
| TITLE | Advanced Computer Vision for Artificial Intelligence | ||||||||||||||||
| UM LEVEL | 03 - Years 2, 3, 4 in Modular Undergraduate Course | ||||||||||||||||
| MQF LEVEL | 6 | ||||||||||||||||
| ECTS CREDITS | 5 | ||||||||||||||||
| DEPARTMENT | Artificial Intelligence | ||||||||||||||||
| DESCRIPTION | This study-unit covers the fundamentals of how to search for images based on their content, similar to Google Image search. This unit covers various filtering and keypoint detection methods together with the bag of visual words approach. Novel approaches that make use of deep-learning will also be introduced in this study-unit. The study-unit will be useful for students interested in image analytics and artificial vision related to a selection of areas. The focus on AI will be present in this study-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. Hands on experience with technology such as Python, OpenCV, cloud technology and Tensorflow will be gained by the implementation of several important image analysis techniques, which may be applied in different applications. Study-Unit Aims: - Introduce students to a popular and effective paradigm of content-based image retrieval that is widely used in the ever-increasing computer vision applications; - Expose students to how systems, such as Google Image Search, are implemented; - Provide hands-on experience to students with popular tools, such as Python, OpenCV and TensorFlow; - Provide hands-on experience with cloud-based vision tools; - Provide a solid basis for students interested to further their studies in the fields of computer vision, computer graphics, video analytics and human computer interface. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Explain in simple terms the mechanism and rationale of content-based image retrieval tools; - Explain the basic understanding of how the image matching can be performed effectively and efficiently; - Identify which image techniques are mostly appropriate for a given application; - Discuss the benefits and limitations of the covered techniques in real-life applications; - Discuss the difference between supervised, unsupervised and hybrid learning approaches in the context of artificial vision. 2. Skills: By the end of the study-unit the student will be able to: - Use the paradigm taught in study-unit to perform image matching in different applications; - Compare the appropriateness of a number of keypoint description methods to solve simple problems; - Implement various state-of-the-art image processing techniques. Main Text/s and any supplementary readings: Main Texts: - Richard Szeliski, “Computer Vision: Algorithms and Applications”. University of Washington, 2022. Supplementary Readings: - Thomas B. Moelsund. Introduction to Video and Image Processing. Springer, 2012. - Tim Morris. Computer Vision and Image Processing. Palgrave Macmillan, 2004. |
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| STUDY-UNIT TYPE | Lecture, Independent Study and Project | ||||||||||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Dylan Seychell (Co-ord.) |
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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 2023/4. It may be subject to change in subsequent years. |
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