| CODE | CCE5215 | ||||||||||||||||
| TITLE | Computer Vision | ||||||||||||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | ||||||||||||||||
| MQF LEVEL | 7 | ||||||||||||||||
| ECTS CREDITS | 10 | ||||||||||||||||
| DEPARTMENT | Communications and Computer Engineering | ||||||||||||||||
| DESCRIPTION | The study-unit covers topics in computer vision including Feature detection and matching, Image segmentation, Object detection, recognition and localisation, Optical Flow, Stereo Vision, Depth estimation, Multi-object Tracking, and Pose estimation. These topics form the tools necessary to tackle a wide range of applications in computer vision. The study-unit assumes knowledge in image processing, pattern recognition and machine learning, including deep learning in neural networks. Study-Unit Aims: The aim of this study-unit is to provide the student with the theoretical background needed to be conversant in the field of computer vision and to design and implement computer vision systems to address real-world problems. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Understand and describe the field of computer vision including tasks and sub-tasks; - Construct and analyze image features; - Understand and describe the image segmentation problem; - Design and develop mathematical models for segmentation; - Develop and apply techniques for object detection; - Develop and apply techniques for object recognition and localisation; - Discriminate between the object detection and localisation models; - Understand and describe the process of optical flow in videos; - Develop and apply stereo vision and depth estimation models; - Understand and describe the problem of multi-object tracking and re-identification; - Develop and apply object tracking models; - Develop and apply techniques pose estimation. 2. Skills: By the end of the study-unit the student will be able to: - Distinguish, select and design computer vision algorithms suitable to address real-world problems; - Analyze and Select the functions required to implement the algorithms; - Implement the algorithms on a general-purpose computer; - Evaluate the performance of the developed algorithms; - Analyze the design, implementation and evaluation of the algorithms. Main Text/s and any supplementary readings: Main Texts: - Simon J. Prince, “Computer Vision Models, Learning and Inference,” Cambridge University Press, 2012. - Richard Szeliski, “Computer Vision: Algorithms and Applications”, 2nd Edition, Springer, 2022. Supplementary Readings: - Jan Erik Solem , “Programming Computer Vision with Python: Tools and algorithms for analyzing images” 1st Edition, O’Reilly. - E. R. Davies , “Computer Vision: Principles, Algorithms, Applications, Learning”, 5th Edition, Academic press. - Journal papers that will be referenced in class. |
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| ADDITIONAL NOTES | Pre-requisite Qualifications: Image processing, machine learning | ||||||||||||||||
| STUDY-UNIT TYPE | Lecture | ||||||||||||||||
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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 2025/6. It may be subject to change in subsequent years. |
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