The module aims to introduce fundamental concepts and techniques in pattern recognition relevant to computer vision. Through this module, students will develop the skills necessary for conducting and reporting pattern recognition experiments in computer vision, and be equipped with the ability to translate pattern recognition knowledge into real-world problem-solving skills. Aligned with the broader undergraduate programme, this module fosters the development of analytical and problem-solving skills critical for both further education and entry-level industry positions. Through this course, students will gain foundational experience in evaluating techniques, enhancing system performance, and making informed design decisions—skills that will prepare them for future studies in machine learning, computer vision, and related disciplines.
A. Evaluate the fundamental principles and mathematical foundations of pattern recognition as applied to computer vision tasks. B. Implement classic and contemporary pattern recognition techniques to solve practical computer vision problems. C. Analyse and solve a given computer vision problem through the selection of appropriate pattern recognition methodologies, justifying their selection based on theoretical and practical constraints. D. Design and validate functional pattern recognition systems for specific computer vision applications, optimising for stated objectives such as accuracy, computational efficiency, or robustness. E. Critically assess the performance and limitations of pattern recognition techniques applied to computer vision tasks, discussing potential areas for improvement.
This module will be delivered through a combination of formal lectures, hands-on laboratory sessions, and three structured coursework assignments distributed throughout the semester. Formal lectures will provide foundational knowledge and theoretical understanding, covering key concepts and contemporary techniques in pattern recognition. Laboratory sessions ensure that students can apply theoretical concepts in a practical environment. By working with datasets and employing various recognition techniques, students gain valuable experience in solving real-world computer vision problems. Three strategically distributed assignments will compel students to explore, design, and evaluate pattern recognition algorithms.