Computer Vision

AI3201 Machine Learning

3 hours Two requisites

MTH1441 Linear Algebra

3 hours No requisites

1 Introduction to Computer Vision: Overview of computer vision, its applications, key concepts and terminology

2 Image Formation and Representation: Understanding image formation models, color representation and image formats

3 Basic Image Processing Techniques: Image enhancement techniques (contrast, brightness), spatial filtering and convolution

4 Geometric Transformations: Translation, rotation, scaling, and affine transformations as well as homography and perspective transformation

5 Feature Detection and Description: Edge detection (Canny, Sobel), Corner detection and feature descriptors (SIFT, SURF)

6 Image Segmentation: Thresholding techniques, region-based and clustering methods (K-means, Watershed)

7 Motion Analysis: Optical flow estimation, motion tracking algorithms

8 Introduction to Machine Learning in Vision: Role of machine learning in computer vision and overview of supervised and unsupervised learning

9 Deep Learning Fundamentals: Introduction to neural networks as well as training and evaluating deep learning models

10 Convolutional Neural Networks (CNNs): Architecture of CNNs and applications of CNNs in image classification

11 Object Detection Techniques: Popular object detection algorithms (YOLO, SSD), evaluation metrics for object detection

12 Image Classification and Recognition: Techniques for image classification, transfer learning and fine-tuning pre-trained models

13 Advanced Topics in Computer Vision: Image generation using GANs (Generative Adversarial Networks), 3D vision and reconstruction techniques

14 Ethical Considerations in Computer Vision: Societal impacts of computer vision technologies, privacy concerns and responsible AI practices

15 Course Review: Review of key concepts and future directions in computer vision

1.1 Mapped to: K1

Explain the fundamental principles, theories, and techniques of computer vision, including image formation, representation, and processing methods.

Teaching Strategy Lectures

Assessment Methods Quizzes, Assignments, Exams

1.2 Mapped to: K2

Utilise advanced image processing techniques to manipulate and analyse visual data, demonstrating proficiency in filtering, enhancement, and geometric transformations.

Teaching Strategy Lectures

Assessment Methods Quizzes, Assignments, Exams

1.3 Mapped to: K1

Evaluate the role of machine learning algorithms, particularly deep learning models, in addressing computer vision problems such as object detection and classification.

Teaching Strategy Lectures

Assessment Methods Quizzes, Assignments, Exams

1.4 Mapped to: K4

Critique the ethical, legal, and societal implications of computer vision technologies and their applications, ensuring responsible practices in the deployment of these systems.

Teaching Strategy Lectures

Assessment Methods Quizzes, Assignments, Exams

2.1 Mapped to: S1

Create and execute algorithms for advanced image processing tasks, demonstrating proficiency in techniques such as filtering, segmentation, and feature extraction.

Teaching Strategy Lectures (include in-class demonstration)

Assessment Methods Assignments

2.2 Mapped to: S2

Implement machine learning techniques, particularly deep learning models, to address and resolve complex computer vision problems, including object detection and classification.

Teaching Strategy Lectures (include in-class demonstration)

Assessment Methods Assignments

3.1 Mapped to: V1

Assess and apply ethical principles in the use of computer vision technologies, recognising the societal impacts and implications of AI solutions in various contexts.

Teaching Strategy Lectures

Assessment Methods Quizzes, Assignments, Exams

Assessment	Weight





Quizzes	20%
Assignments	30%
Midterm exam	15%
Final Exam	35%
Total	100%

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Computer Vision

Computer Vision

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